tag:blogger.com,1999:blog-69154054150036667022024-03-19T14:17:44.215+05:30N Recursions... a consciousness sojourning the void ...Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.comBlogger303125tag:blogger.com,1999:blog-6915405415003666702.post-81292226987025286662024-03-10T17:32:00.001+05:302024-03-10T17:32:11.376+05:30Websites offering ChatGPT-like functionality without needing to login<p>Being able to use ChatGPT for free is cool. It does get annoying that one has to login to use it. When you are in a hurry, and want to ask something to an LLM, there are some websites you can quickly access by keeping them bookmarked.</p><h2 style="text-align: left;"><a href="https://sdk.vercel.ai/" target="_blank">Vercel's AI playground</a></h2><p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1HO0G0PEs4Okn53TCbA7tGJnKcReFy5kKhH-yqjYL5btsgx8faz5vkned9U5lCxTbnMWC05XyTTbrpi87UPMkzisprtTxgi8-5tidKbhsJeNlbwbEnKPiho08UNRggisNiWiPzsW1l0fJQQr-auHNMqMK7PnS7P1JTE-1opSRZJpjV6WVx2pK060scMQ/s622/vercel.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="399" data-original-width="622" height="205" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh1HO0G0PEs4Okn53TCbA7tGJnKcReFy5kKhH-yqjYL5btsgx8faz5vkned9U5lCxTbnMWC05XyTTbrpi87UPMkzisprtTxgi8-5tidKbhsJeNlbwbEnKPiho08UNRggisNiWiPzsW1l0fJQQr-auHNMqMK7PnS7P1JTE-1opSRZJpjV6WVx2pK060scMQ/s320/vercel.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">https://sdk.vercel.ai/</td></tr></tbody></table><br /></p><p>This is my favourite. It not only uses ChatGPT, it simultaneously generates an output with Llama too.</p><p> </p><h2 style="text-align: left;"><a href="https://huggingface.co/chat/" target="_blank">HuggingChat</a> and <a href="https://huggingface.co/spaces/olivierdehaene/chat-llm-streaming" target="_blank">HuggingFaces Spaces</a><br /></h2><p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikw17xzETh_4dLewqhl_01KuNaG0j8KTM7z02o0X-Qdfdns7Z_o0C4tSFzrTExyFh0OHWuTQf5xNDl1c3zykfOFa3HByjjSRDuIWa0cQ1FqkVMaakZRJWPdkDJnZLPMq5bajhICA0y01XoW2R6b3LBDVpS4GTVcDl_yuagXvQqYjiKdwLqC2FWMqY-5qY/s622/huggingChat.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="431" data-original-width="622" height="222" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEikw17xzETh_4dLewqhl_01KuNaG0j8KTM7z02o0X-Qdfdns7Z_o0C4tSFzrTExyFh0OHWuTQf5xNDl1c3zykfOFa3HByjjSRDuIWa0cQ1FqkVMaakZRJWPdkDJnZLPMq5bajhICA0y01XoW2R6b3LBDVpS4GTVcDl_yuagXvQqYjiKdwLqC2FWMqY-5qY/s320/huggingChat.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">https://huggingface.co/chat/ and https://huggingface.co/spaces/olivierdehaene/chat-llm-streaming<br /></td></tr></tbody></table><br /></p><p>HuggingChat is a page that opens without much of a hassle, but it allows only 3 prompts. In settings you can choose from various models.</p><p>HuggingFaces spaces allows typing more prompts and even allows you to easily change the "p mass", which appears to be the temperature, but might be top p.<br /></p><p> </p><h2 style="text-align: left;"><a href="https://talkai.info/chat/" target="_blank">Talk AI</a></h2><p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhFqra4s0yx3a8w53U-9NSROXkZ155NY1X4Q_FwCRM7Ywz33o7l_Ljyz8aw8OIBmoYWm8t5qxRwCRVU4BGUoAlUs1HsRpLZdc79y9HgmfSVWy8pTzH8O5QXsz7xqhJhXSNAsp0v8d3klZhmb4jJhFN1hIQweOareawAI_fzgCii5_XqaCOg4OXdgKJT82o/s620/talkAI.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="443" data-original-width="620" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhFqra4s0yx3a8w53U-9NSROXkZ155NY1X4Q_FwCRM7Ywz33o7l_Ljyz8aw8OIBmoYWm8t5qxRwCRVU4BGUoAlUs1HsRpLZdc79y9HgmfSVWy8pTzH8O5QXsz7xqhJhXSNAsp0v8d3klZhmb4jJhFN1hIQweOareawAI_fzgCii5_XqaCOg4OXdgKJT82o/s320/talkAI.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">https://talkai.info/chat/</td></tr></tbody></table><br /></p><p>This begins with a slightly irritating human verification that needs a manual input, and has a 30 prompt limit, but the chat interface is ok.<br /></p><p><br /></p><h2 style="text-align: left;"><a href="https://chataigpt.org/" target="_blank">Chat AI GPT</a></h2><p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguIBSzB357obqCRP-D5seSAobRp8uoR6LXcQEVvqGQhXMTnHxmxP9lQ_Dw4RwXpHweXxxGWn62l9xV6AllCsL6H8LZPlZmXhbacUQCU4AmVuaVXOuOVgN-VApcJqBOUU7ZCiwxGpWPjR-2psWHZP7fTWDmX3dBDi6CrDIhT0c2lmdSH73iCes6mWOCx-I/s622/chatAIGPT.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="542" data-original-width="622" height="279" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguIBSzB357obqCRP-D5seSAobRp8uoR6LXcQEVvqGQhXMTnHxmxP9lQ_Dw4RwXpHweXxxGWn62l9xV6AllCsL6H8LZPlZmXhbacUQCU4AmVuaVXOuOVgN-VApcJqBOUU7ZCiwxGpWPjR-2psWHZP7fTWDmX3dBDi6CrDIhT0c2lmdSH73iCes6mWOCx-I/s320/chatAIGPT.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">https://chataigpt.org/</td></tr></tbody></table><br /></p><p>Even this begins with a human verification, but it automatically disappears. The chat interface is ok.</p><p><br /></p><h2 style="text-align: left;"><a href="https://chat.lmsys.org/" target="_blank">LM Sys</a></h2><h2 style="text-align: left;"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj_Jgvh-s0ZQRXVkFHZcA-I_XjV1KTeaBO1BksMq-JnvQk9F4KItLbRI6eGSntnXB1D3tZkPYDvCs65W0gaZRVEfO3i7SEJY-pd5ngvr9fFHq2xkATiFdvPBtdKiztKgzN5FBXLqla0fw-Ay1QKNL6xU2QO_ZhCb4EG3cpMs1bo207t38bcQgOUEI1TET8/s621/lmSys.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="481" data-original-width="621" height="248" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj_Jgvh-s0ZQRXVkFHZcA-I_XjV1KTeaBO1BksMq-JnvQk9F4KItLbRI6eGSntnXB1D3tZkPYDvCs65W0gaZRVEfO3i7SEJY-pd5ngvr9fFHq2xkATiFdvPBtdKiztKgzN5FBXLqla0fw-Ay1QKNL6xU2QO_ZhCb4EG3cpMs1bo207t38bcQgOUEI1TET8/s320/lmSys.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">https://chat.lmsys.org/</td></tr></tbody></table></h2><p>This site has the annoying human verification delay and needs you to click on a usage terms agreement. The good part is that it allows you to compare models and allows changing various parameters like temperature, top P and max output tokens.<br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-36964580633183833292024-02-28T22:45:00.006+05:302024-03-06T02:15:55.857+05:30Use a free, local LLM instead of GitHub CoPilot<p>There was a time when Google and StackOverflow replaced the need to buy books to refer documentation. Now we have entered the era of LLM's (Large Language Models) <a href="https://blog.continue.dev/its-time-to-collect-data-on-how-you-build-software/" target="_blank">replacing Google and StackOverflow for coding</a>. We've also entered the era of frameworks like <a href="https://skypilot.readthedocs.io/en/latest/" target="_blank">SkyPilot</a> assisting with batch jobs on the cloud. <a href="https://blog.continue.dev/what-llm-to-use/" target="_blank">Choosing an LLM</a> itself is like choosing an ice cream in a buffet of ice creams. To ease my programming, I decided to try a few VS Code extensions similar to GitHub CoPilot. The objective was to find extensions that worked locally without sending any of my data to an external server, and were free to use. </p><p><b></b></p><blockquote><b>CAUTION: VSCode extensions aren't checked by anyone for safety, so install it only if you really trust the creator of the extension. Even for the ones mentioned below, check first.</b></blockquote><p></p><p>There's <a href="https://huggingface.co/spaces/mike-ravkine/can-ai-code-results" target="_blank">a leaderboard</a> of how well models can write code. </p><h2 style="text-align: left;">Instruct model versus base model</h2><ul style="text-align: left;"><li><b>Base models:</b> These aren't designed for answering questions. These are meant to provide completions. If you prompt it with "What is the capital of India", it'll respond with "?indiacapitalisdelhi#code ends here". Notice how it outputs the question mark. It only generates a completion based on the data it was trained with. Base models are used to generate code while you type.<br /></li><li><b>Instruct models: </b>The instruction tuned models are designed to answer questions. Asking it for the capital of India would return something like "The capital of India is New Delhi. It serves as the center of government for the country and...". The instruct models are used for question-answer type of interaction with the LLM.</li></ul><p> </p><h2 style="text-align: left;">Using Ollama for the local LLM instead of OpenAI's API<br /></h2><p>Download and <a href="https://ollama.com/download" target="_blank">install</a> Ollama using `<i><b>curl -fsSL https://ollama.com/install.sh | sh</b></i>`. It installs to `<b>/usr/local/bin</b>` and creates a systemd service. You can also build ollama and take <a href="https://github.com/ollama/ollama/blob/main/docs/development.md#linux-rocm-amd" target="_blank">advantage of ROCm</a> for AMD inbuilt GPUs. There are <a href="https://github.com/ollama/ollama/issues/690" target="_blank">some ways to stop</a> the service. Downloaded models get stored in `<b>usr/share/ollama/.ollama/models/blobs</b>`, and you can <a href="https://github.com/ollama/ollama/blob/main/docs/faq.md" target="_blank">change the directory</a> by changing the OLLAMA_MODELS environment variable. You can access it on <a href="http://localhost:11434/" target="_blank">localhost</a> via port <b>11434</b>. Some other commands:</p><ul style="text-align: left;"><li><span style="font-family: courier;"><b>systemctl list-unit-files | grep ollama</b></span> will show you <b>if it's running</b>.</li><li><span style="font-family: courier;"><b>systemctl status ollama</b></span> will show you more <b>info about the process</b>.</li><li><span style="font-family: courier;"><b>sudo systemctl stop ollama.service</b></span> will <b>stop the process</b>.</li><li><span style="font-family: courier;"><b>sudo systemctl disable ollama.service</b></span> will <b>disable auto-startup</b>.</li><li><span style="font-family: courier;"><b>sudo systemctl start ollama.service</b></span> to <b>start the process</b>.</li><li><span style="font-family: courier;"><b>sudo systemctl enable ollama.service</b></span> will <b>enable auto-startup</b>.</li><li><b><span style="font-family: courier;">ollama list </span></b>will show a list of the downloaded models.</li><li><b><span style="font-family: courier;">ollama pull <modelname></span></b> will download any model listed <a href="https://ollama.com/library" target="_blank">here</a>. Remember to use a <b>smaller model for getting a faster response</b>. This is especially important when you are using only CPU.</li><li><b><span style="font-family: courier;">ollama rm <modelname></span></b> will delete a downloaded model.</li></ul><p> </p><h2 style="text-align: left;"><a href="https://github.com/rjmacarthy/twinny" target="_blank">Twinny</a> </h2><p>So far, Twinny has been the only local LLM that has worked fine, is well designed and supports multiple models. You can open Twinny's sidebar by pressing <b>Ctrl+Shift+t</b>. You can use the <b>Alt+\</b> key combo to generate code completions. You can stop the code generation using <b>Ctrl+Shift+/</b>. The code completions are called FIM (Fill In Middle). Do take note that to generate code completions, you need to use models called "base models". Don't use "instruct models" for code completions. To make the suggested code in grey become actual code, you need to press <b>Tab</b>.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiKKUVgJENeS9S8FLAKPNapYW16eRs9uzR3rsmBpmYzKGgjJzqeZpAU66p3JJfmsfyVxme-Ay1eNHdeZS-HYETsCrKBsFNtg41eux7NiMWDY-hK0k8HX8hPlWLp6RLP9EWR1EotfPF5pZswBcwwlX6-I8mtdJRQHLgPlnHLq3bz_Ka8NfrFlTrmeGzIZlI/s742/twinnyFIM.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="387" data-original-width="742" height="334" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiKKUVgJENeS9S8FLAKPNapYW16eRs9uzR3rsmBpmYzKGgjJzqeZpAU66p3JJfmsfyVxme-Ay1eNHdeZS-HYETsCrKBsFNtg41eux7NiMWDY-hK0k8HX8hPlWLp6RLP9EWR1EotfPF5pZswBcwwlX6-I8mtdJRQHLgPlnHLq3bz_Ka8NfrFlTrmeGzIZlI/w640-h334/twinnyFIM.png" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Here, the stable-code model is used for fill-in-middle code suggestions<br /></td></tr></tbody></table><p>You can also use the Twinny sidebar to chat with it, similar to how you chat with ChatGPT. For this, you need to use "instruct models". You can even highlight code in the main editor and ask Twinny to explain the code or generate code based on the highlighted code. For this functionality, the model specified in <b>Chat</b> is used. Chat can sometimes not understand the User or it produces incomplete outputs. This depends on how the model is prompted, which model is used and how much processing power is available. I'll update this section if I find out more.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUkpQA3bY7WE0BpLjLsx6oGMewVL_0EmeWDmeIJsjmQvmFP9JThwv9aphCE8EQyXQEIuqpu7EmCbMhMVTY-WtZsQkALVJIK962caOIETcpnTBwSxPKk2rz9TMrqzsDNbvz6xvXkvufL9XvtdY3WEFEe1OKoYJwiB_aSwVPdufjIDtOR3zGyaTzqrDvWcs/s584/twinnyInstruct.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="584" data-original-width="279" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjUkpQA3bY7WE0BpLjLsx6oGMewVL_0EmeWDmeIJsjmQvmFP9JThwv9aphCE8EQyXQEIuqpu7EmCbMhMVTY-WtZsQkALVJIK962caOIETcpnTBwSxPKk2rz9TMrqzsDNbvz6xvXkvufL9XvtdY3WEFEe1OKoYJwiB_aSwVPdufjIDtOR3zGyaTzqrDvWcs/s16000/twinnyInstruct.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">For this chat, the `codellama:latest` model was used.<br /></td></tr></tbody></table><p> </p><p>There is also <a href="https://www.markhneedham.com/blog/2023/10/18/ollama-hugging-face-gguf-models/" target="_blank">a way to use models from HuggingFace</a>.<br /></p><p><br /></p><h2 style="text-align: left;"><a href="https://marketplace.visualstudio.com/items?itemName=Continue.continue" target="_blank">Continue</a><br /></h2><p>The other almost-good extension I found was this. It can work by connecting to one of Continue's servers, or you can <a href="https://continue.dev/docs/model-setup/select-model" target="_blank">configure it to work with a local LLM</a> too. Continue sends telemetry data to its server, so you need to go into the settings to <a href="https://continue.dev/docs/walkthroughs/running-continue-without-internet" target="_blank">disable it</a>.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCzLYafJQ1SXcT2NY5cqwB3626Nin05silkUPnrydw-toJTTR-LjkISkiXKZ1xEr6T0LCqhysxMNkav8FL03jD5tj7CKrN4RoNMJ9MYj7bvTZOJSKWNQSeBZkHMWUGNmqY0BiUkqQeButKeNgTSmj9Z00ldzCLundD9kNPvncgClw0WuOD-3kq0kIZUko/s632/Continue_unawareOfCodeEntirety.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="569" data-original-width="632" height="360" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiCzLYafJQ1SXcT2NY5cqwB3626Nin05silkUPnrydw-toJTTR-LjkISkiXKZ1xEr6T0LCqhysxMNkav8FL03jD5tj7CKrN4RoNMJ9MYj7bvTZOJSKWNQSeBZkHMWUGNmqY0BiUkqQeButKeNgTSmj9Z00ldzCLundD9kNPvncgClw0WuOD-3kq0kIZUko/w400-h360/Continue_unawareOfCodeEntirety.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Continue didn't account for the entire code when adding new code<br /></td></tr></tbody></table><p>As shown in the screenshot above, I selected a function and asked Continue to add an error message if a file was not found. It did a poor job of adding new code. The entire code barely had 30 lines. This showed me that Continue was poorly programmed. There is of course a sidebar where you can type prompts and have code explained or generated, but I uninstalled Continue.<br /></p><p>It connects to this server:</p><p></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0X3_l_20mED3fMXJ33gVmdWVzvN4JBQ3tqMN1tZMPWN_nEyaxSHlHlULv_clwvbcuDWn_TPqsr3GSKwYv3su7-IJ1sozKmXGZm4f9fh1jAIHuIzVArK9EXuRTfp4aTWytkggbylQUwqoIC5zGQiQ-AJ8IRBDrqMFT-FghKKGB6bVV8LxJNrUNfJxECr8/s440/continueConnectingToWeb.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="149" data-original-width="440" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0X3_l_20mED3fMXJ33gVmdWVzvN4JBQ3tqMN1tZMPWN_nEyaxSHlHlULv_clwvbcuDWn_TPqsr3GSKwYv3su7-IJ1sozKmXGZm4f9fh1jAIHuIzVArK9EXuRTfp4aTWytkggbylQUwqoIC5zGQiQ-AJ8IRBDrqMFT-FghKKGB6bVV8LxJNrUNfJxECr8/s16000/continueConnectingToWeb.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Running `nslookup node-proxy-server-blue-l6vsfbzhba-uw.a.run.app` showed the server it connected to<br /></td></tr></tbody></table><p><br />Continue still has many bugs that need to be resolved. Also, I couldn't fully trust it to run entirely locally, so I uninstalled it.</p><p><br /></p><p></p><p></p><h2 style="text-align: left;">Other local LLM extensions</h2><ul style="text-align: left;"><li><a href="https://github.com/RussellCanfield/wingman-ai" target="_blank">Wingman-AI</a>: It's good, but it didn't support the `stable-code` model, so I uninstalled it.</li><li><a href="https://marketplace.visualstudio.com/items?itemName=matthoffner.backseat-pilot" target="_blank">Backseat pilot</a>: Poorly explained on how to use. Meant to use llama cpp python. Uninstalled it immediately</li><li>Open copilot: Needs cody and llama cpp. Uninstalled it.</li><li><a href="https://github.com/danielgross/localpilot/tree/main" target="_blank">Local pilot</a>: Requires a Github <a href="https://github.com/danielgross/localpilot/issues/25" target="_blank">copilot account</a>.</li><li>llama coder: Didn't generate anything. No interface to work with.</li><li>Tabby: Needs Docker Tabby server. Works on CPU and CUDA.</li><li>Your copilot: Doesn't work out-of-the-box. Meant for OpenAI API. Giving it Ollama's URLs didn't work.</li><li>Ollama autocoder: No interface. Not straightforward to use. Auto completion didn't work with the default space+pause or ctrl+space.</li><li>Wingman: Uses LM Studio or Openai APIs. Can't use at work without their permission. Likely uses telemetery. Has a nice set of prompt UIs for mode, prompt, placeholders. caters to general programming, creative writing and technical writing. The appimage didn't work on Linux.</li><li>Ollama copilot: Incompatible with latest vscode version.</li><li>Refact.ai: The local version needs docker with NVidia GPU.</li></ul><p><br />If you don't want to pay for an OpenAI API but still want to use ChatGPT3.5's free chat, a person created <a href="https://github.com/HalilCan/headless-chatgpt" target="_blank">Headless ChatGPT</a>.<br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-45228624152969687492024-01-01T15:29:00.000+05:302024-01-01T15:29:00.179+05:30Get rid of Google's annoying sign in popup<p style="text-align: center;"> <br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFqNucZ9YRaJpdcuSZepbBtHBKHrp6y0TQJhZSj8w5ZcK7OX6s8SYASU5UlhvGxzDchPu6nXQR5-zBfyKvcR3egEhS4hgtNL-2u3vkJ9mwcXOCW_PB2FgE5W-9o5zhOg9Bn_lDvvVRjNDgyVD3KcIMqfk0FOVhBpxlomnDCQhlXeRg-SI4Hxdh1Aa0G5A/s300/googleSignInPopup.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="220" data-original-width="300" height="220" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFqNucZ9YRaJpdcuSZepbBtHBKHrp6y0TQJhZSj8w5ZcK7OX6s8SYASU5UlhvGxzDchPu6nXQR5-zBfyKvcR3egEhS4hgtNL-2u3vkJ9mwcXOCW_PB2FgE5W-9o5zhOg9Bn_lDvvVRjNDgyVD3KcIMqfk0FOVhBpxlomnDCQhlXeRg-SI4Hxdh1Aa0G5A/s1600/googleSignInPopup.png" width="300" /></a></div><br /><p>It's like a horror movie ghost giving you a jump-scare each time you open a website. Google somehow thinks it'd be a great idea to take away focus from whatever we are doing, and present us with the most annoying thing on the internet: The Popup!</p><p>Thankfully, it's possible to get rid of this. </p><h2 style="text-align: left;"><a href="https://www.reddit.com/r/firefox/comments/yoo6oa/how_to_disable_sign_in_with_google/" target="_blank">On Firefox</a>:</h2><p>1. Install the Adguard extension.</p><p>2. Enable Adguard to run even in private tabs (if you use private tabs).</p><p>3. In Adguard's preferences, select "Filters" and then enable the "Annoyances" toggle button.</p><p style="text-align: center;"><img alt="" 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/></p><p></p><p>4. Now click on the "Annoyances" section to view all the annoyances that are to be blocked, and enable any of the annoyances you want to get rid of (for example, the "AdGuard Popups filter").</p><p>Some details on using uBlockOrigin instead, are given <a href="https://support.mozilla.org/si/questions/1405122" target="_blank">here</a>. <br /></p><h2 style="text-align: left;"><a href="https://support.google.com/accounts/thread/219332922/how-to-stop-obnoxious-google-sign-in-prompts?hl=en" target="_blank">On Google Chrome</a>:</h2><p>This of course works only if you are signed in. Direct <a href="https://myaccount.google.com/connections/settings?hl=en&pli=1" target="_blank">link</a>.<br /></p><p>1. Go to "My Account" : "Security" ( https://myaccount.google.com/security ).<br />2. Click on "See all connections" link in "Your connections to third party apps & services".<br />3. Click on the gear at the top of the "Third party apps & services" page.<br />4. Disable the "Signing in with Google" prompt.</p><p> </p><p>That's it. Enjoy your popup-free browsing! Some other people have given the same solutions <a href="https://webapps.stackexchange.com/questions/169172/how-do-i-prevent-websites-from-prompting-me-to-sign-in-with-google/172551#172551" target="_blank">here</a> and <a href="https://www.howtogeek.com/735152/how-to-turn-off-the-sign-in-with-google-prompt-on-websites/" target="_blank">here</a> too.<br /></p><p></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-73830547015676208402023-11-08T22:59:00.003+05:302023-11-23T14:42:37.014+05:30Ten minute email id<p>Ever been to a website that required you to enter your email id to sign up for even a short trial, and doing so, exposed you to their spammy newsletters and promotional emails? Well, 10minutemail is here to rescue you from that.<br /></p><p><img alt="" height="389" 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" width="640" /></p><p>I came across this interesting concept https://10minutemail.com/ (there's also https://10minutemail.net/), which allows you to keep extending the 10 minute limit. If you refresh the page, you won't lose that email id until the 10 minutes expires. </p><p>I tried sending an email from my real email id to the address shown, and sure enough, within a minute, 10minutemail showed the test email in its inbox. You'll have to scroll down for it.</p><p>It's interesting that people actually created such a concept.</p><p><b>Tips:</b> </p><ul style="text-align: left;"><li>Did you know that GMail ignores the dot in an email id? For example, if you have an email id as <b>brad.pitt@gmail.com</b>, you can write it as <b>bradpitt@gmail.com</b>, and the email will still reach the same person.</li></ul><ul style="text-align: left;"><li>Did you know that you can add anything after a plus symbol, and those parts get ignored by GMail? For example, if you subscribe to a newsletter from vox, you can provide your email id to them as <b>bradpitt+vox@gmail.com</b>. The "+vox" will be ignored, but when you receive the email, the email id will be shown as "bradpitt+vox@gmail.com". This makes it much easier to filter emails and recognize where it's coming from.</li></ul>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-14167793970500368052023-10-11T18:40:00.001+05:302023-10-13T12:13:28.825+05:30What to do when your RAM causes your computer to freeze and crash?<p>What would you do if you were simply browsing the internet on your web browser, and the computer froze? You try to move the mouse and it doesn't. Keypresses aren't registered. But you can see all the apps on the screen as they are. Nothing responds to input though. So you are left with no option other than to reboot, and then everything works normally, which leaves you puzzled. </p><p>When this happened to me, I checked the <i>dmesg</i> log, the <i>kern</i> log, the <i>sys</i> log, and found nothing that indicated that the computer crashed. However, when I used <b><i>last -x</i></b>, I saw there was a crash registered: <b><i>tty7 20:36 - crash (00:19)</i></b>. Such a crash can be caused due to a faulty RAM, so I ran Memtest86+, and sure enough, the test failed. </p><ul><li>With XMP switched off, the tests passed. </li><li>With just one RAM stick, the tests passed even with XMP. </li></ul><p>While considering going through the long cycle of handing over the RAM to the service center and them testing it and replacing it, I decided to ask a few queries on the motherboard company's forum, because it seemed like a power distribution issue.</p><p>As the conversation progressed, I realized that the problem wasn't with the RAM. The problem was with XMP. The helpful person in the forum advised updating the BIOS and trying a higher, but safe CPU NB/SoC voltage (it's usually used for tuning memory frequency, but can raise CPU temperature if too high). When Memtest failed with this setting too, but with the BIOS update, when Memtest succeeded during the first pass and failed in the second pass, the person realized that I was on the edge of success. So he advised trying 3000MHz instead of 3200MHz. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj9gZ-b9jbXcH8GKSHPdP01lM1w47rJ0aYqag6ySbNX7sfV0JbiteupWMSUdgKB2HMFkjO3sev8-39xza1yPK6li4L0JwGz4fYtBA0jgHxU10PTWdJHNVuAX0aa2P7vSDJjmIN2rsQ9NxkpPdYwqTWR9dIocOeQaysgPfC4Pu5e5gFVcvvIuVm4Fy-NPVA/s400/test_passed_memtest.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="227" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj9gZ-b9jbXcH8GKSHPdP01lM1w47rJ0aYqag6ySbNX7sfV0JbiteupWMSUdgKB2HMFkjO3sev8-39xza1yPK6li4L0JwGz4fYtBA0jgHxU10PTWdJHNVuAX0aa2P7vSDJjmIN2rsQ9NxkpPdYwqTWR9dIocOeQaysgPfC4Pu5e5gFVcvvIuVm4Fy-NPVA/s16000/test_passed_memtest.jpg" /></a></div><br /><p></p><p>This worked perfectly. With 3000MHz, there was no crash. This was far better than the 2133MHz that the RAM would have worked at if XMP was switched off. Now that everything worked fine, there was no need to go to the service centre too, since I know the problem was with the RAM frequency, and not with the RAM itself.</p><p><b>Some things I learnt:</b></p><ul style="text-align: left;"><li>Updating the BIOS is important. Some issues got fixed when I did the update.</li><li>Memtest86+ is a fork of Memtest86, and the guys developing Memtest86 are continuing to maintain it, so it may be a better option than Memtest86+.</li><li>Plenty of other factors can affect RAM (the mainboard model (PCB layer count, PCB trace optimization, RAM slot
topology and slot count, component selection, RAM VRM etc.), the mainboard's BIOS optimizations and the BIOS settings, the CPU's integrated memory controller (IMC), quality depends on the CPU
generation as well as on the individual CPU (silicon lottery), the properties of the RAM modules), it's also possible that the board/BIOS lacks proper optimization for it.</li><li>Problems often appear if you increase the stress on the memory system.
So, with one module, everything is ok, with two modules, you double the
stress on the memory system (and again when going from two to four
modules). Or you go from the safe default speeds to the XMP speeds that
the RAM advertises with.</li><li>About that XMP speed: The RAM maker will have verified that in-house, so
there's rarely a doubt that the RAM (in isolation) can achieve that
speed. For example, the highest-spec DDR4 you can buy has an XMP profile
of DDR4-5333 at a staggering 1.6V. Is this RAM cabable of that speed
under the right circumstances? Yes. Will it work at that speed in a
run-of-the-mill board model and CPU that is not handpicked? Unlikely.</li><li>If any one of the components of the memory system is not entirely happy
with the speed and timings you're trying, it will fall down like a house
of cards. Often times, the RAM kit itself is not the culprit, it's just
the combination of everything that doesn't quite work well together for
some reason. The first order of business is to look at the important
voltages, and try some tweaks there. It might be a matter of BIOS
optimization, even to the point where you have to open a ticket with the motherboard manufacturer.</li><li>The voltage stability of the PSU's output will not be affected by any
peripherals. It can take a hit when you install a graphics card with a
high power draw, as their power draw nowadays can be higher than any CPU
you can buy. But for entry-level or mid-range graphics cards with
<200W power draw, there should be no concern. Fans and DVD drives and
USB devices don't even cause a blip, they often in the single-digit
Watt range.</li><li>About XMP Profile1 and profile2 in the BIOS, I realized from <a href="https://www.cgdirector.com/xmp-memory-profile-guide/" target="_blank">this</a> page: "Profile 1 is the profile that offers better stability but might come at the cost of slightly looser timings". Whereas "Profile 2 loads a memory’s complete default XMP profile, including all the advanced timing parameters. Timings would be tighter in this case, but stability mileage would vary from motherboard to motherboard".</li><li>A faulty RAM <a href="https://superuser.com/questions/1153819/is-there-any-protection-from-ram-corrupt-in-file-copy-routines-in-windows" target="_blank">could have possibly corrupted</a> many files in your system, so it's advisable to reinstall the operating system and all other software. Unfortunately, even your personal files may be affected. <br /></li></ul>
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</div>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-44754433640639662452023-10-11T11:15:00.007+05:302023-10-11T16:07:57.277+05:30Making it easier to merge multiple backups of your phone's contacts<p>I had a lot of backups of my phone's VCF files, and I wanted to merge it. There are a few software available for doing the merge, but they don't allow the User to select from ambiguous merges. So I decided to build one.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNIyBHvvs13-BXw1pKCDNU_4OXtY1_MQIV3fDNIxraFgY-oMvOf0EUgslzn-3kZLSwP6EkSHP0r_CzNjdGkkMtcKASJlopd6fMwyekHuyeHsL4RNI36XzmA1BefncCFoEJpFBuhcQp0M4gSzkXDJkuFh_1d3VRwTTzxnBtyRrdMcI3sH8FP_97leOwx4w/s874/GUI1.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="663" data-original-width="874" height="485" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNIyBHvvs13-BXw1pKCDNU_4OXtY1_MQIV3fDNIxraFgY-oMvOf0EUgslzn-3kZLSwP6EkSHP0r_CzNjdGkkMtcKASJlopd6fMwyekHuyeHsL4RNI36XzmA1BefncCFoEJpFBuhcQp0M4gSzkXDJkuFh_1d3VRwTTzxnBtyRrdMcI3sH8FP_97leOwx4w/w640-h485/GUI1.png" width="640" /></a></div><p>My VCF contacts merger is designed to search for duplicates based on the last 8 characters of any line starting with "TEL". </p><ul style="text-align: left;"><li>It automatically merges obvious contacts, and shows the User any ambiguous contacts. </li><li>Editing can be done with the usual Ctrl+C, Ctrl+V, Ctrl+X, and you can move between duplicate contacts using the left and right arrow keys. </li><li>You can even save, close the GUI and resume from where you left off, when you open it again.</li></ul><p>Hope it's useful for anyone who wants to merge their VCF files.</p><p>The program: <a href="https://github.com/nav9/VCF_contacts_merger">https://github.com/nav9/VCF_contacts_merger</a><br /></p><p><br /></p>
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</div>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-45865986448276156112023-09-06T23:56:00.000+05:302023-09-06T23:56:00.136+05:30top for viewing GPU usage in Linux<p>When you need to monitor CPU consumption in Linux, you can use top or htop. But when you run a machine learning program, and want to confirm whether it is actually using the GPU instead of the CPU, how would you do it for an AMD GPU on Linux?</p><p>This is where you do: <b>sudo apt -y install radeontop</b></p><p>and run it with: <b>radeontop -c</b><br /></p><p></p><div class="separator" style="clear: both; text-align: center;"></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOhaZcTtvocT81bsaHXuTo8_lJlT2wc5KJsciJcd-LBPUHN9byTHfI9NREOg1r--vs7_xgCnJVJkz7k7alEHiQazCxICAXJFAIKJquh8r39kJj3Hhpo_KFWxFxnUWeDtpIbRxUHI6TDijsWPpcPFoQy8lRiEJ8Us9bqgFx_Y-Rng3owDf7Ecd0cZ0V9pk/s642/radeontop.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="411" data-original-width="642" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOhaZcTtvocT81bsaHXuTo8_lJlT2wc5KJsciJcd-LBPUHN9byTHfI9NREOg1r--vs7_xgCnJVJkz7k7alEHiQazCxICAXJFAIKJquh8r39kJj3Hhpo_KFWxFxnUWeDtpIbRxUHI6TDijsWPpcPFoQy8lRiEJ8Us9bqgFx_Y-Rng3owDf7Ecd0cZ0V9pk/s16000/radeontop.png" /></a></div><p></p><p>This works even for the GPU on the AMD processor (called an APU). To monitor NVIDIA GPU usage there are different tools. To know more about radeontop, type: <b>man radeontop</b>.<br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-56768693895737244452023-08-03T13:47:00.003+05:302023-08-03T13:47:00.134+05:30PipTrends is like GoogleTrends to know which Python package is popular<p>When searching for whether Dynaconf was the best choice for config files, I came across this website named <a href="https://piptrends.com/compare/dynaconf-vs-pydantic-vs-configparser" target="_blank">Pip Trends</a>, which I was quite impressed with (that it existed). I assume the website has a storage limit, so it shows only the download trends for the past 6 months. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5qcgA9xuQNXjE2x_hLEO28ko2kAANIZ7ufE1WkT7R1IvDNiNj4Ven8ddIqI3VcJw1tyOy8i3p6Gjs8SZC6yGEoovkvBiu6N4TqFFlDuHNDYcFtlg6cLkpZOIeZGJChLZEKbFR2mQLJnphgq5HRic2WfBS5hvcdcoZfx6FPwTMOpUGHqva4_7XOWZm/s795/pipTrends.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="670" data-original-width="795" height="270" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5qcgA9xuQNXjE2x_hLEO28ko2kAANIZ7ufE1WkT7R1IvDNiNj4Ven8ddIqI3VcJw1tyOy8i3p6Gjs8SZC6yGEoovkvBiu6N4TqFFlDuHNDYcFtlg6cLkpZOIeZGJChLZEKbFR2mQLJnphgq5HRic2WfBS5hvcdcoZfx6FPwTMOpUGHqva4_7XOWZm/s320/pipTrends.png" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Pip Trends indicates popularity of Python packages<br /></td></tr></tbody></table><p>The website even shows the number of GitHub stars, open issues and other useful information which can help in decision making. <br /></p><p>Surprisingly, Dynaconf was at the bottom of the download trends. I had initially zeroed in on Dynaconf because it seemed to follow the <a href="https://12factor.net/config" target="_blank">twelve factor app</a> guidelines. However, looking at the popularity of Pydantic (<a href="https://progressstory.com/tech/python/configuration-management-python-pydantic/" target="_blank">seeing that</a> it's good for config management too), it looks like I'll have to evaluate the functionality of both, in order to decide.</p><p>Kudos to the creators of PipTrends. A very necessary vacuum filled in the world of software comparisons. </p><p>Similarly, there's also <a href="https://npmtrends.com/n-vs-nave-vs-nodeenv-vs-nodenv-vs-nvm" target="_blank">npmtrends</a>.<br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-49764906312254316652023-07-22T21:17:00.000+05:302023-07-22T21:17:11.537+05:30When I used analytics to resolve a dispute<div style="text-align: left;">Long back, I used to travel by office cab, and the person who got off the cab last in the evening, happened to be the "route monitor", who would decide the best route the cab takes. Over a period of time, he observed areas where traffic was frequently causing more delays, and suggested variation in the route. Since the new route required a female employee to get off the cab earlier and walk along a long stretch of lonely road, she protested, and the matter went to HR and Admin, as a women's safety issue. Meetings were held, where they reached a stalemate because nobody could prove anything. </div><p style="text-align: left;">Observing that there was a lack of data, I stepped in and asked for two weeks to objectively analyze the route timings. The first week, we took the new route, and the second week we took the old route. This is the rough picture of the data I gathered:</p><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3HRBa1tn-_RNkmPaW5qC2G0BiDdTIQ9ho6y2lDZI5IEqbUYuzjOyxH0xz-vT3KVU5aQrXauVL-Nfx0xxI3hqzVpg8RDq53TvT5Nj89K0RgNjKakMLcvtgWAlxD7wBItBuNbTHqu13Cq_kol5CMTR1kr0fKwxZETamB12NTuoSZpAvHOOeqi5vLIR5dEw/s1237/cabRouteAnalytics.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="882" data-original-width="1237" height="456" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3HRBa1tn-_RNkmPaW5qC2G0BiDdTIQ9ho6y2lDZI5IEqbUYuzjOyxH0xz-vT3KVU5aQrXauVL-Nfx0xxI3hqzVpg8RDq53TvT5Nj89K0RgNjKakMLcvtgWAlxD7wBItBuNbTHqu13Cq_kol5CMTR1kr0fKwxZETamB12NTuoSZpAvHOOeqi5vLIR5dEw/w640-h456/cabRouteAnalytics.png" width="640" /></a></td></tr><tr align="center"><td class="tr-caption">Route analytics. Click image to view it larger.<br /></td></tr></tbody></table><br /><p style="text-align: left;">At first, I noted the timings only for a few prominent points where colleagues were dropped off (grey circles). As I noticed a fluctuation in time, I added more landmarks. The results were surprising.<br /></p><p style="text-align: left;">When the cab reached the point shown by the blue square, we had the option of taking <b>route 1</b> <b>(the new route suggested)</b> or <b>route 2</b> <b>(the old route)</b>. Both routes would eventually lead to the point shown by the yellow circle. The route monitor's assumption was that the time delay between the blue and yellow points, was what caused everyone else to reach home late. However, the data revealed an entirely different picture. These were the points I noted:<br /></p><ul style="text-align: left;"><li style="text-align: left;"><b>Route 1 avoided initial delay:</b> The blue and yellow points are consistently closer for route 1. So there was indeed some time saved by avoiding route2's traffic signal.</li><li style="text-align: left;"><b>Route 1's initial time saving futile:</b> On the Thursday of Route 1, we reached the final point (my stop) at 7:50, despite there being a very short time delay between the blue and yellow points.</li><li style="text-align: left;"><b>Route 2's initial time loss insignificant:</b> Even though the Thursday of Route 2 took up some time between the blue and yellow points, I still reached early, a little after 7:30.</li><li style="text-align: left;"><b>First point mattered: </b>The very first grey circle was the first landmark we reached after leaving office and crossing a frequently jammed stretch of road. The slight fluctuation of the first point appeared to match with the massive fluctuations of the other points on the same horizontal line. This indicated that there were certain days when more people in other companies left their office sooner, causing more jams at various parts of the city, which caused delays at various points.</li><li style="text-align: left;"><b>Rain irrelevant:</b> The rain didn't cause any significant delay.<br /></li><li style="text-align: left;"><b>Travel after sunset:</b> One of the complaints raised was that the lady would sometimes have to walk on that stretch of road after sunset, which made her uneasy. The diagram above captures the various times of the year sunset happens, and the arrows show a few other colleagues who also had to travel a bit in the dark after getting off the cab to reach home.</li><li style="text-align: left;"><b>Insufficient data:</b> The patterns showed great fluctuation, which led me to conclude that we needed more data to make a proper conclusion. However, the existing data was sufficient to make a preliminary conclusion.</li></ul><h2 style="text-align: left;">Resolution</h2><p>We concluded that we could continue using the old route. However, the initial arguments between both parties and their rallying of other colleagues for support, created a gloomy mood in what was earlier a happy group. During the two weeks, I noticed people attempting to influence the outcome of the dispute. Still, in the end, the data showed everyone that it was better to verify facts than make assumptions. It led to a healthy resolution. The incident also showed that no matter how capable a leader is (like the route monitor), there will come situations when the team goes against the leader. Such situations need to be handled carefully and early, before it snowballs into larger problems that have long-term effects on morale.<br /></p><p>When people are willing to go into details, the facts often provide enough reason to not launch into unnecessary turmoil. Data analytics can even provide surprising results for companies. For example, one company assumed that people would purchase raincoats or flashlights during hurricanes. However, the data showed them that <a href="https://nrecursions.blogspot.com/2014/06/conference-analytics-for-startups.html" target="_blank">people were purchasing strawberry pop-tarts</a>!<br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-38197033344633461212023-07-12T19:47:00.006+05:302023-07-14T17:48:24.825+05:30Generative AI with Large Language Models: Part 3<div style="text-align: center;"><br /></div><p style="text-align: left;">Continued from <a href="https://nrecursions.blogspot.com/2023/07/generative-ai-with-large-language_9.html" target="_blank">Part 2</a>, this blog post continues with what I learnt from the Coursera course on Generative AI.<br /></p><p style="text-align: left;"><i><b></b></i></p><blockquote style="text-align: left;"><i><b>LICENSE: Images shown in this blog post and following parts of the blog are screenshots taken from the <a href="https://www.coursera.org/learn/generative-ai-with-llms" target="_blank">Generative AI course</a>. You may not use or distribute these or the text content for commercial purposes. It was created by <a href="https://www.deeplearning.ai/">DeepLearning.ai</a>, and is licensed under a <a href="https://creativecommons.org/licenses/by-sa/2.0/legalcode" target="_blank">creative commons license</a>.</b></i></blockquote><p style="text-align: left;"></p><p style="text-align: left;">Here are some of the main points: </p><h2 style="text-align: left;">Reinforcement Learning from Human Feedback (RLHF)</h2><div style="text-align: left;">RLHF fine-tunes the LLM using human feedback, to ensure that the model is better aligned with human preferences. It can also be used to personalize LLMs to individual users.<br /></div><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: left;"><tbody><tr><td style="text-align: center;"><img alt="" height="271" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Fine tuning with human feedback<br /></td></tr></tbody></table><h2 style="text-align: left;"> </h2><p style="text-align: left;">LLMs trained on vast data can have toxic language in their completions, reply in combative and aggressive voices, and providing detailed information about dangerous topics. The human values, of <b>helpfulness, honesty, and harmlessness</b> are sometimes collectively called <b>HHH</b>, and are a set of principles that guide developers in the responsible use of AI. Additional fine-tuning with human feedback helps better align models. </p><p style="text-align: left;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhByZ9nGfBe5HIhBEGAdZgBqslQVldzIv3YwXo0IOosVhk8YXqtp8nJauMkW0wOyH-yumyDFEVZmLDP88KpNF-CMSfTqdH9pzsRTqt87q_UdUDmMu4RuUAjVD1QJ3WLbMW4gKSTVH97AV30vJbk3c7NR2UHPJwrsox4iB0lzQTiHZoE5us9e8ciKsRG1o8" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="319" data-original-width="821" height="248" src="https://blogger.googleusercontent.com/img/a/AVvXsEhByZ9nGfBe5HIhBEGAdZgBqslQVldzIv3YwXo0IOosVhk8YXqtp8nJauMkW0wOyH-yumyDFEVZmLDP88KpNF-CMSfTqdH9pzsRTqt87q_UdUDmMu4RuUAjVD1QJ3WLbMW4gKSTVH97AV30vJbk3c7NR2UHPJwrsox4iB0lzQTiHZoE5us9e8ciKsRG1o8=w640-h248" width="640" /></a></div><p><br />In RLHF, human labelers score a dataset of completions by the original model based on alignment criteria like helpfulness, harmlessness, and honesty. This dataset is used to train the reward model that scores the model completions during the RLHF process. Since human evaluation is time consuming, a Reward Model can also be used. In the context of language modeling, the sequence of actions and states is called a <b>rollout</b>...as opposed to the term "playout", in classic reinforcement learning.</p><p>A prompt database is used, where prompts are fed to the LLM to produce completions. The completions are ranked by humans, about how good the response is, in the order of how helpful the completion is. The same sets of data will be ranked by multiple humans, to establish consensus and minimise the effect of humans with poor judgement. The labeled datasets are then used to build a reward model which will be capable of eventually doing this evaluation instead of the humans. Human labelers are selected from a diverse global population.</p><p>An example of the instructions given to human labelers:</p><p></p><blockquote><span style="font-size: x-small;">Rank the response according to which one provides the best answer to the input prompt. What is the best answer? Make a decision based on 1. The correctness of the answer, 2. The informativeness of the response. For 1, you are allowed to search the web. Overall, use your best judgement to rank answers bases on being the most useful response, which we define as one which in at least somewhat correct, and manually informative about what the prompt is asking for. If two responses provide the same correctness and informativeness by your judgement, and there is no clear winner, you may rank them the same, but please only use this sparingly. If the answer for a given response is nonsensical, irrelevant, highly ungrammatical/confusing, or does not clearly respond to the given prompt, label it with "F" (for fail), rather than its rank. Long answers are not always the best. Answers which provide succinct, coherent responses may be better than longer ones, if they are at least as correct and informative.</span></blockquote><p></p><p>After completions are ranked, completion pairs are created. For each pair, you will assign a reward of 1 for the preferred response and a reward of 0 for the less preferred response. Then you'll reorder the prompts so that the preferred option comes first. This is an important step because the reward model expects<br />the preferred completion, first. Now, the human responses will be in the correct format for training the reward model. Note that while thumbs-up, thumbs-down feedback is often easier to gather than ranking feedback, ranked feedback gives you more prompt completion data to train your reward model. <br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="112" 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style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Ranked and paired completions<br /></td></tr></tbody></table><p style="text-align: center;"></p><p><a href="https://arxiv.org/abs/2009.01325" target="_blank">When the reward model is used</a> independent of humans, we feed it the prompt completions and try to minimize the loss, which is the log sigmoid of the difference between each of the reward values generated by the reward model. Once trained, the reward model can be used as a binary classifier which provides a set of logits across the positive and negative classes. Logits are the un-normalized model outputs before applying any activation function. Applying softmax to the logits will give a probability value.<br /></p><h3 style="text-align: left;">Fine tuning the LLM with RL and the Reward model<br /></h3><p style="text-align: center;"><img alt="" height="303" 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width="640" /></p><p>Start with a model that already has good performance on your task of interests.<br />First, you'll pass a prompt from your prompt dataset. In this case, "a dog is...", to the instruct LLM, which then generates a completion. In this case "a furry animal". Next, you send this completion, and the original prompt to the reward model as the prompt completion pair. The reward model evaluates the pair based on the human feedback it was trained on, and returns a reward value. A higher value such as 0.24 as shown above, represents a more aligned response. A less aligned response would receive a lower value, such as -0.53. You'll then pass this reward value for the prompt completion pair to the reinforcement learning algorithm to update the weights of the LLM, and move it towards generating more aligned, higher reward responses. Let's call this intermediate version of the model the RL updated LLM.<br />These series of steps together forms a single iteration of the RLHF process. These iterations continue for a given number of epochs. If the process is working well,<br />you'll see the reward improving after each iteration as the model produces text that is increasingly aligned with human preferences. You will continue this iterative process until your model is aligned based on some evaluation criteria. For example, reaching a threshold value for the helpfulness you defined. You can also define a maximum number of steps, for example, 20,000 as the stopping criteria. <br /></p><p>A popular choice for the RL algorithm is PPO (Proximal Policy Optimization). <br /></p><h3 style="text-align: left;">Proximal Policy Optimization<br /></h3><p>PPO optimizes a policy (the LLM) to be more aligned with human preferences. "Proximal" means that the updated LLM is close to the previous version of the LLM. You begin with the Instruct LLM. The PPO then goes through two phases. In Phase 1, it creates completions. In Phase 2, it does a model update.</p><p><b>Phase1: </b>The expected reward of a completion is estimated through a separate head of the LLM called the "value function". Assume you generate many reward values using the reward model, for the various completions of a prompt. The value function estimates the estimated total reward for a state "s" (estimating the total future reward based on the current sequence of tokens). This gives a baseline to evaluate the quality of completions against an alignment criteria.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="216" 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" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Trying to minimize value loss. V_theta(s) is the value function<br /></td></tr></tbody></table><p style="text-align: center;"></p><p><b>Phase 2: </b>We make a small change to the model and evaluate the impact of the update to your alignment goal. Model weight updates are guided by the prompt completion losses and rewards. PPO ensures that model updates are within a small "trust region". Such small (proximal) updates move the model toward higher rewards. The PPO policy objective is to find a policy whose expected reward is high (more aligned with human perferences).</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="56" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Policy loss<br /></td></tr></tbody></table><p></p><p>Pi_theta is the model's probability distribution over tokens. The denominator is the probabilities of the next token with the initial LLM. The numerator is for the updated LLM. A_hat_t is called the Advantage term which estimates how much better or worse the current action is, compared to all possible actions at that state. We look at the expected future rewards of a completion following the new token,<br />and we estimate how advantageous this completion is compared to the rest. Maximising the Advantage term gives a better aligned LLM. Let's consider the case where the advantage is positive for the suggested token. A positive advantage means that the suggested token is better than the average. Therefore, increasing the probability of the current token seems like a good strategy that leads to higher rewards. This translates to maximizing the expression we have here. If the suggested token is worse than average, the advantage will be negative. Again, maximizing the expression will demote the token, which is the correct strategy. <br />The portion of the equation within clip's paranthesis, is the "trust region". It defines a region in proximity to the LLM, where our estimates have small errors. This is because the advantage estimates are valid only when the old and new policies are close to each other.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" 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style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Entropy loss<br /></td></tr></tbody></table><p style="text-align: center;"></p><p>The entropy loss helps ensure creativity. The temperature setting influences creativity at inference time. But entropy influences it during training. </p><p style="text-align: center;"><img alt="" height="153" 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width="400" /></p><p>The PPO objective updates the model weights through back propagation over several steps. Once the model weights are updated, PPO starts a new cycle. For the next iteration, the LLM is replaced with the updated LLM, and a new PPO cycle starts. After many iterations, you arrive at the human-aligned LLM. Q-learning is<br />an alternate technique for fine-tuning LLMs through RL. Direct Preference Optimization, is a simpler alternate to RLHF.</p><h3 style="text-align: left;">Reward Hacking</h3><p>Reward hacking is when the agent learns to cheat the system by favoring actions that maximize the reward received even if those actions don't align well with the original objective. In LLMs, reward hacking can manifest as the addition of words or phrases to completions that result in high scores for the metric being aligned, but reduces the overall quality of the language. This happens because the LLM weights are being updated after PPO. To mitigate this, a copy of the LLM is frozen and used as a reference. We calculate the difference (to check how much the updated model diverged from the reference) in completions between the reference LLM and the RL updated LLM, using KL divergence. KL divergence is calculated for each generated token across the whole vocabulary off the LLM. This can easily be tens or hundreds of thousands of tokens. However, using a softmax function,<br />you can reduc the number of probabilities to much less than the full vocabulary size. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="302" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Using PEFT to reduce memory footprint<br /></td></tr></tbody></table><p>Using PEFT, you can use the same LLM as a reference and only update the weights of a path adapter, not the full weights of the LLM. This means that you can reuse<br />the same underlying LLM for both the reference model and the PPO model,<br />which you update with a trained path parameters. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="270" 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NQ/8jf4UbPhJ/wA9NQ/8jf4U+aXeX3C5V2Qz/hFPhr/0Nsn/AH2n/wARR/winw1/6G2T/vtP/iKft+En/PTUP/I3+FG34Sf89NQ/8jf4Uc0u8vuDlXZDP+EU+Gv/AENsn/faf/EUf8Ip8Nf+htk/77T/AOIp+34Sf89NQ/8AI3+FG34Sf89NQ/8AI3+FHNLvL7g5V2Qz/hFPhr/0Nsn/AH2n/wARR/wifw1/6G2T/vtP/iKft+En/PTUP/I3+FG34Sf89NQ/8jf4Uc0u8vuDlXZDP+ET+Gv/AENsn/faf/EVYsvCnw6W/tmh8VSPKsqFELp8zBhgfc9ai2/CX/npqH/kb/Cp7JfhV9vtvIkvjN5qeXu83G7Ixn8cUnKVt5fcNJX6Hr0/3oO37+P/ANCFa9ZE/wB+H/rvH/6EK168s7QooooAKKKKACiiigAooooAK5/xbqj2Oli3hYrcXbeUhXqq/wATfgOPqRXQV554nuzd+IJlzlLZRCuPU4Zv1wP+A10YWl7SqomtGHPNIx1VUUKoAUDAA7UtFFfRpW0R6ewUVzY1C7/4WN/Z3nt9k/s7zfKxxv34z+Val14g0iySV7nUbeIRSeU4Z+Q+M7cdc4NZxqxd79NCVNdTQoqC2v7S8sxd21zFLbEE+arDbgdefaqll4i0bUbk29nqdtNN/cV+T9PX8Krnj3K5l3NKiuZsvGGn3HiHULKTUbMW8XlLbMHH7xmB3YOecHArZuta0yy+0far6CH7Pt80O2Cm77ufr2pRqwavcSnF63LtFVIdV0+4e2WK7idrqMyQAH/WKOpFSPeW0V5FaSTotxKpaOMnlgOpFVzR3uPmRPRWdf8AiDSNLlWG+1G3glbojt831I7D61neIPFNppdnZS295alrm4iCkuCDEW+Z+vQAHmolVjFNt7EucUr3OioqC0vbXUIPPs7mK4hzt3xsGGR15FT960TT1RSd1cdDcyWV1DeQ5Mtu29R/eH8S/iMivU7a4iu7WK4hbdFKodG9QRkV5VyOa7LwTeGTTriybrbS/J/uNyP13D8K8vMaOiqI5MVDTmOooooryDiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKhu7qGytJbq4cJDEpd2PYCpq43xpqBkmg0qM/KAJ58HtnCD8wT/wABFaUqbqTUUVCPM7I569vp9Tv5L65G12G1I858pOy/XuT3P0FQCqmp3y6Zpd1fvGZFt4zIyqcEgVit4tkt7SO9v9CvrWwYKxudyOqq3QkKcgcjtX0KdOilA9NONNcp0tFCkOodSCrDII6EUuK2NL3QlFFGKYBRRRigQdqRlDqVYZB4pcZNZ2n6qL7VNUshCUNhIkZbdnfuXd07VMpRTSfUHbZno/hTW3v4Hsbpi11bgEOessfQMfcdD+B710leU212+nXsF/HktbtuYf3k6MPy5+oFeqI6yxq6EMrAFSO4NeDjKHsqmmzPOr0+SWmw6iiiuMwCuG+IsUl5feD9PRGZZddhmk2jOFjVm59s4ruaMdaYzzebV7Twn8Ute1DX2lgttQs7ZbC58l5EIQMHjBUHDbjnHesdP7QubTWoY9NddT8aXbiC2uYji1s1QR+fMP4Ttydp5JKj1r2CigDyjw8r+D/CPjHw1LFI15pcU1xDciM77yKRCY3J7uMbD6bRVdI08Ky/DrUtWilGlWWlPBJMImZba4eNMMwAJGRuGcV6/RQB5r431m38V+Ckg06C8e1u9WtLUSyW7Isq+arMygjJQYxkjHFWNd1O30r4x6Vcak0sVr/ZEkNs4hdw87zKCg2g87VFehUdKAPK/HMGq2nxAtYtF+SfxPpzabJID/qDG4Yzevyxu+PfFei2dpZ+H9DitLSJks7GDaiIpZtqjsByTx9Sarx6BAvimbxBLNJNcG2W1gjbGyBM7m2+7HGT7AVrigDyrw7NaweLdPt/BGqXs+j3XnS6hYTRMYLIFSyspYAxsXwNmTnJ4GKd8PPFGnaB4XtPDt9Bff8ACQwTyR3Fklq7SySNISZM4wVIOd2cYr1OigDy/RvEVn4N1vxZba5FdjUbvVHu7VY7d5GvIWVRGsZAIJGNuO1Zth4evIJ/Aem6pa4muNTvNYu4duVhfazopPTILKPqDXsVFAHlvjEQ2HxNttU1nUdR03SZdKNtBeWYOEl8zc6MwViu4bSCMZx1rrvBkekLpM8ui2d5Dbyzs7T3iuJLtsDMuXO9gegLY6cDFdJRQB5PpHiPR/Dvi7xlquqxXUd7eX4gtQtpI7XKRIqhYyFwTuzxn0zWTd6FfaNp/hjVdbN7YQSareajqb2Y3tZyTg+WT8p4UcE44JNe3UUAeTapd6BD8P8AxhqOiLqN1JJYGKXVrzzGM5YFQqu/JAz0UBRmr/ia1Oi23gK9uIpH0jR5l+2FIy4i/cbEkKjnCt37Zru9Y0iz13TJNO1CMyWsjIzoGK52sGAOO2QKv96APNZdQHiXxcPE+mxyto+haZciO6MTJ9pnkAyqAgFgqr19TiqMGgXifs4jTdNtpDe3GnCZ4kGJJC7B5B6lipI/SvWKKAPM/DVz4S1DVtOktLjWtbvYF3R/akcx6eAmCSu1Y0P8IwC2eldxoGvWfiTR4dUsFnW3lLKonjMbfKSDwfpWp9KKQgooooAKKKKACiiigArGu2jSTUWlG6JeXGM5XyxkVs1lSANeXakAguAQe/yLTEeLjxR8L2AI8LzY/wCuY/8Ai6X/AISb4Yf9CvN/37H/AMXXqv8AYeg4/wCQXpv/AH4j/wAKP7D0H/oF6b/34j/wrsVaHZ/eYeyl3R5V/wAJN8MP+hXm/wC/Y/8Ai6P+Em+GH/Qrzf8Afsf/ABdeq/2HoP8A0C9N/wC/Ef8AhR/Yeg/9AvTf+/Ef+FHt4dn94eyl3R5V/wAJN8MP+hXm/wC/Y/8Ai6P+Em+GH/Qrzf8Afsf/ABdeq/2HoP8A0C9N/wC/Ef8AhR/Yeg/9AvTf+/Ef+FHt4dn94eyl3R5V/wAJP8MP+hXm/wC/Y/8Ai6D4n+GAGf8AhF5v+/Y/+Lr1X+w9C/6Bem/9+I/8KP7C0L/oF6b/AN+I/wDCj28Oz+8PZy7r7jIsvBXg++sbe7i0G28ueNZU3Bs4YZGefep/+Ff+Ev8AoA2n5N/jXQI0MaKiGNUUAKqkAADsKXzY/wDnon/fQrndSpfRs05YHPf8K/8ACX/QBtP/AB7/ABp0XgTwrDMkseh2qujBlIB4IOQetb/mx/30/wC+hS+ZHkDenP8AtCl7Sp3Y+WHYZMTug/67x/8AoQrYFY1wQhhZmCgTx5JOP4hWp9qt/wDnvF/32KzLJaKi+1W//PeL/vsUfarf/nvF/wB9igCWiovtVv8A894v++xR9qt/+e8X/fYoAloqL7Vb/wDPeL/vsUfarf8A57xf99igCWiovtVv/wA94v8AvsVymrfETSdD8QtpV9HMqBFcXMWHXnsQOR+GaYHXsQqknoBk15IsrXJa5b707tKfqxJ/rXoTa7p2qaDfXGl30FyVt5GAjcEg7T1HUfjXn0ahYUUDACgY/CvSyyPvSZ14Vati0UUV7B3HIj/krH/cJ/8AZ6PDFrbv4r8U3DwxvMt2EV2UEhSMkA9ug/Kt/wDsi1Gu/wBsfvPtfkfZ/vfLsznp65pbHSLbT7u+uYC/mXsgll3NkbgMDHpXLGjLmu+7Zj7N3+ZyemaUNUt/GOjQP9njkvSIyo4QkZ6DtkflV+w1K80/UdO0rW9JtY5JQY7S8tcFCQvIwRleP51qjw7YhdSXM4GoyCWYrKVIYdCpHIqOy8NW1pfRXs13e31xCCsLXk2/ygeu0YAyfXrUqjNNNb/pclQkrWMzQLaA+N/FQaCMhXtyoKD5fkPT0ptnYWt38R9aluIUlMMEBjDjcqkr1x0z2z7mtW58M2dxrLamlxe280m3zlt5yizbem7FXYNLt7fV7vUk3+fdKiSZPGF6YFUqL0Vutxqm9n3MPUESP4heH0RVRBaXAVVGB06AU7UcH4kaED1+yXGRWprGhWmtLA07zwz27FoZ7eTZJGT1wfeobLwvp9lqUGoq9zLexKy+fPMXaQMMfMT6dgMAUSpS1S2bTBwd7LuZXgaKO4ttUvbiNXv5b+VbhnUFhgjC+wA7UeM7S2hs9Ejitoo0XVIFCqgAAycjHpWld+FrO4v5r2C5vbGacfvzZzmMS+5GOvvU134c0660VNKZZUt4mDxukh8xHBzuDHnOSfzpeyl7NwsHI+Tlsaojjh+WNERfRVwKWqmnWK6bZ/Z1nubg7izSXMpkck+/9Kt11R2RstgNbXhGYw+ItnRbiBlPuVII/QtWL2q9okqQ+I9Nd2CgysmT7xtWGLjzUpGdZXgz0ykqIXNuB/r4v++xR9qt+08X/fYr5s8smrP1e7mtLRHgZFdpAmWXIA57Z9q5i1+KOgvqNxY33m2UkMzxeY43xttYjO4dM47itrVby11DTLeazuYbiIzrh4nDDoe4prcZV/tXU/8An4g/78f/AGVH9q6n/wA/EH/fj/7KuC8QL45OszHRWh+wbV8vd5ec45+9z1zWYU+J396D/wAg12rDprdGLq2drM9R/tTU/wDn4g/78f8A2VH9qan/AM/EH/fj/wCyry3b8Tv78H/kGjb8Tv78H/kGn9WXdC9t5M9R/tXU/wDn4g/78f8A2VH9q6n/AM/EH/fj/wCyry7Z8Tv70H/kGjZ8Tv70H/kGj6su6H7b+6z1H+1NT/5+IP8Avx/9lS/2pqf/AD8Qf9+P/sq8u2fE7+9B/wCQaNvxO/vQf+QaPqy7oXtv7rPT21XVApP2iDgZ/wBR/wDZV0drI01pDK+Nzxqxx6kV5V4ZHi0T3X/CRmMweV+627M7s/7PtXptjcQDT7YGeMYiTjePQVz1oKDsjSMrq9i7RUX2m3/57xf99ij7Tb/894v++xWJRLRUX2mD/nvH/wB9iuY17x/pnhzWobC9imaOWESieHDhTkjBHXt+tAzrK8t1G4N3q9/cnHzzsqkf3V+Ufy/Wu90/xHpOsWzzabfw3G1SxRWwwx6qeR+VeaWjb7OFsEb0Dc+4z/WvRy2N6jl2OjCq8mzN8W/8ihq3/Xq/8q5ia81a90zSvD97b2+nWuo26Ri8Ehl3qFHyAYAVyOma7TVbBdT0q6sGkMa3EbRlwM4yOuKrXuhW2oaAmkzsxRI0RJRwyMowHHoeK9GrSlKV12OqcG5XRja3cTJrenaDAl99iW1M0i2TASuFO1V3ZGB645qA2usvZazaWw1SC2+zCaze6lzKkynJQMGJKnA6n1rau9Bluo7Gf+0ZI9Us0KJepGPnB6hkPBB/nVvTbO9tWkkvtTkvZHwADGsaIB/dUd/ck1PspOWouRt6nKatr17qFnp13pcrxra2a6ndKhOHXIHlnHriTj2rYgvW1LxZcPDdMmn2NkpOG+UyS/MGPY4QDr61Y0Xw1aaKL9Ed5ku3JKuMBE5wg9huP51HpXha203QbvSTPLNHdbhJK3DbSu0D8FAAojTq3uxKE9znLu9gsTZX2matqt3N9sjjmll8xreZGbBHI2fTFaklpdav4x1mwfUruCxiigfy7eQoxYqeh/hHUkDrxVh/C11cWFtZXesyywWjxtCqQLH9wjG7k7uBjt61q2+lrb63f6mJSWu0jQx44XYCOvvmlGnUb12BQlfUxBC+r+KbvS5by7jsdLt4QEhmZGmdx95mHJwB+dL4Utza694mhM0s2y4hAeVtzEeXxk9/rWhfaDNLqp1PTtSewupIxFNiJZFlUdMg9x61LpGhrpN1f3H2ua5kvHSR2lxkELjt6/p0pxpy502uv4FKL5kzV613ng+5M/h2GNjl7dmgOfRT8v8A47trg8V1vgZ28nUIjkgTK49srj/2WssyinSUuxGKXuXOtooorxDzwrzjU/EHiTxP44v/AAx4VvrfTLbS41N/qMkAmbzG6Rop4+p9j6c+j15L8P8AU7LRfHPxBtdWvYLS4OofaR9ocJui+YhgT1GCPzFMZu/DzxPrGo3+veHfELwzaros6o1xCmxZ42ztbHY8fqK2vF3iK+0K0t49J0a41PUrxjHbxqMRIQMlpX6KoHPvj8a4XwDq+nR6z4s8b6jdxWWmavqCWtjNcMEEwQEAjPrx+R9K9H8R6nBpfh7UbmSeKN0tJZEDuBuKqTx69qAOY+EviHWfE/hCXU9buEnuHvZUjMcYRVRcDAwORnPJrL+J/wARb7Q7a903wyqyalaRLcX10VDR2cZICg54LsSAB6c0z4epqVr8C7aLw41tLrbwPJEkjgBWeQ8n6DkZ6kCuJ8Sab4n0H4cf2De+G7aNtUvoVudQOpCWa7uGfd8w298Y68CgD3zRpLiXQ9PlvH33T20bTNgDLlQTwOnOavVS0l7yTSbZtQtI7S7KDzII5fMWM+gbAzxV2gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiikIKKKKACiiigArGuoxLLqEZfYH+Ut/dBjAzWzWNdIkkmoJIdqP8rHOMAxgE00I8dHw08PY/5Hm1P4p/8XR/wrTw9/0PFp/45/8AF1bHw/8Ah+AAPFH/AJNw/wCFH/CAfD//AKGgf+BcP+FekqmnxP7jj5PJfeVP+FaeH/8AoeLT/wAc/wDi6P8AhWnh/wD6Hi0/8c/+Lq3/AMIB8P8A/oaB/wCBcP8AhR/wgHw//wChoH/gXD/hT9p/ef3ByeS+8qf8K08P/wDQ8Wn/AI5/8XR/wrTw/wD9Dxaf+Of/ABdW/wDhAPh//wBDQP8AwLh/wo/4QD4f/wDQ0D/wLh/wo9p/ef3ByeS+8qf8K08P/wDQ8Wn/AI5/8XR/wrTw/wD9Dxaf+Of/ABdW/wDhAPh//wBDQP8AwLh/wo/4QD4f/wDQ0D/wLh/wo9p/ef3ByeS+8qf8K18P/wDQ8Wv5p/8AF0v/AArXw/8A9Dxa/mn/AMXVr/hAPh//ANDQP/AuH/Cj/hAPh/8A9DQP/AuH/Cl7T+8/uDk8l95U/wCFa+H/APoebX84/wD4up7H4daDDqFtKnjW3kdJUYIrR5Ygg4Hzd6u2Xwx8F6jK0Vlr01zIo3FIbiJiB64ArSt/g7oFvdQzreagzROrgFkwSDn+77VMqsbWc39xUabbul+J22q2VtqdoLO8hE1vNNGrxseCNw9Kq/8ACuPCH/QCt/8Avpv8a0bn/WQf9d4//QhWxXnHWjlf+Fc+EP8AoBW//fTf40v/AArnwh/0Arf/AL6b/GupxRQBy3/CufCH/QCt/wDvpv8AGj/hXPhD/oBW/wD303+NdTRQBy3/AArnwh/0Arf/AL6b/Gj/AIVz4Q/6AVv/AN9N/jXU0UAct/wrnwhj/kBW/wD303+NcxqnwljvdfeSxlt9N0rYoEcal3Ldzg8D869QopgcRD8O9B0PSrya3tmuLxbeTbPO25gdp6AYA/KubRtyKfUA16zIgkjaM9GUg15JChihETffizG31U4P8q9PLH70kdmFerQ+iiivYO0KKKKAILm+tbNoVuJ0iaeQRxBj99j2HvU+DXKeMwPtnhr/ALCsddYayjNuco9iFK7aK9te2t28y286StA5jlCnOxh2PvSXt/aadb/aL25jt4twXfIcDJ6Cuc8F/wDH74l/7CslL8QoEutAtbeTOyXUIEbHXBJBqPbP2TqWJc/c5jqgQRkHIPcVWutQs7FoRdXMcJmcRxBzgux7CsXwpdzQrc6BfPm80xtiuf8AlrD/AAP+XB/CuV1qRte1W110M32C31OCzsgDw43ZeT8SAB9KmeItTUluKVW0brc9NooIwTRXQbIKKKKYBUtpptrrGqWVjewia2kmJkjbOGARjjj3wairX8LRGbxLCe0EUkn4nCj/ANCNYYqVqMmZ1XaDNr/hXHhD/oBW/wD303+NB+HPhD/oBW//AH03+NdTSV82eUjyi0+DvnX88t9frBZmZ2hgthlgm47QWbpxjsa6d/CWi+HLGE6baCOVpVDSsxZ24PUn+ldjWXr0ckllF5UbyFZlYhFycYPb8acdxnl/iHw74p1DWpbnTNc+y2jKoSHznXBA54Ax1rL/AOEQ8cf9DP8A+TEn+FekmO4I/wCPK7/78tR5Vx/z53X/AH5b/Cu5YhpW0MnRi3c82/4Q/wAcf9DP/wCTEn+FH/CH+OP+hn/8mJP8K9J8qf8A587r/vy1HlT/APPldf8AflqPrL8hewj3POIvCPjZJUZvE2VDAkefIcj8q9GxS+Vcf8+d1/35b/Cjy7j/AJ87r/vy3+FROqpbmkIKGwmKMCl8u4/587r/AL8t/hR5dx/z53X/AH5b/Cp5oljH4jcj+6afa/DzwnNZwSyaJbs7xqzEs3JIye9I8dyUYCyuuh/5YtXU2SslhbowKssSgg9jgVlUaexLOd/4Vx4Q/wCgFb/99N/jR/wrjwh/0Arf/vpv8a6misgOW/4Vx4Q/6AVv/wB9N/jXNa78JYL/AFmJtKNtpunLEBIFDO7Pk5IBOOmO9enUUDOM0r4aeH9IjMgge7uwp2zXDZwcdlHArkrTIsoATkiNQT74r2CvKrq3Nnf3dqVC+VM4AH90nK/oRXpZbK02jpwjtJoiqnqOrWWkrC19OsKTyiKNmBILHsfSrtcp43s4dRGh2VwpMM+orG4B5wUYV6tabhBtHZUk1G6OrIx2+tUotVsptVn0yKcNeQIHljAPyg4xz07jiufs/EcmlaDe2+o5l1PS2Fvs73BPERHru4/I1Q8PWLaJ4svGvJS9y2lC6vHznMhkLNj6dB9KxlidYpddyHV2SO7ork7a78S6jo39t291aQpIhmgsWg3BkGSAz5zuI9OKcfEV3rMmk2mkNHbSX1qbuWaRPM8lAcYA7ndkc+lafWI21/4cr2sToF1G3fVX00M32lIROV28bScdfrVquOtHudP8aalNqdwlwYNJWTzY49hZA5PK5IzwenFUh4svW0v+1xq+niTb5o0oRclP7u/rvx+GaiOJS+IXte52jahbrqqaaWP2l4TOoxxtBwefrVrNcx5guPiDp8yAhJNJZxnrguCK6cdK1pzcrlRle4orqvAwyNSYdPMRfxC5/qK5Wu48GweVoQmIwbiV5PqM7R+iiuTMWlSsY4p+5Y6GiiivDPPCsHWfBnhrxDex3er6NaXdxGMLJInzY9CR1Hsc1vUUxmVf+G9F1TTYNNvdLtZrK3dXht2jASMr02gdO4+hpms+FtC8RSQvrGlW160AIiM6btoPXH5CtiigDI0Xwvofh1pm0fSrWyM4AlMCbd+M4z+Zq1qGk6fqwtxf2kVwLaZbiHzFzskXow9xmrtFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRSEFFFFABRRRQAVj3ES3E1/C+dknyNjrgoAf51sVjXDmOa/kVN7J8wX1IjBxQtwODHwa8MgYE2o4/wCuq/8AxNH/AApvwz/z31H/AL+r/wDE1jj4oeLCM/8ACIkf9s5v8KX/AIWf4r/6FL/yHN/hXcliO/4nK3R7Gv8A8Kb8M/8APfUf+/q//E0f8Kb8M/8APfUf+/q//E1kf8LP8V/9Cl/5Cm/wpP8AhZ/iv/oUj/36m/wp2xPf8QvR7Gx/wpvwz/z31H/v6v8A8TR/wpvwz/z31H/v6v8A8TWP/wALP8V/9Ckf+/U3+FH/AAs/xX/0KR/79Tf4UWxPf8QvR7Gx/wAKb8M/899R/wC/q/8AxNH/AApvwz/z31H/AL+r/wDE1j/8LP8AFf8A0KR/79Tf4Uf8LP8AFf8A0KR/79Tf4UWxPf8AEL0exsf8Kb8M/wDPfUf+/q//ABNH/Cm/DP8Az31H/v6v/wATWP8A8LP8V/8AQpH/AL9Tf4Uf8LP8V/8AQpH/AL9Tf4UWxPf8QvR7HYeG/AGj+FtQe+sJLppmjMWJXBGCQewHoK6qvJh8T/Fef+RS/wDIU3+FTWnxK8Uz39vDJ4TKpJIqM3lTcAnB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style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Evaluating the human aligned LLM<br /></td></tr></tbody></table><p style="text-align: center;"></p><p>Once you have completed your RLHF alignment of the model, you will want to assess the model's performance. You can use the summarization data set to quantify the reduction in toxicity. The number you'll use here is the toxicity score,<br />this is the probability of the negative class, in this case, a toxic or hateful response<br />averaged across the completions. If RLHF has successfully reduced the toxicity of your LLM, this score should go down. First, you'll create a baseline toxicity score<br />for the original instruct LLM by evaluating its completions off the summarization data set with a reward model that can assess toxic language. Then you'll evaluate your newly human aligned model on the same data set and compare the scores.<br />In this example, the toxicity score has indeed decreased after RLHF, indicating a less toxic, better aligned model. <br /></p><h2 style="text-align: left;">Scaling human effort to evaluate prompts <br /></h2><p>Labeled data set used to train the reward model requires large teams of labelers to evaluate many prompts each. One idea to overcome these limitations is to scale through model self supervision. <b>Constitutional AI</b> is one approach of scale supervision. Constitutional AI is a method for training models using a set of rules and principles that govern the model's behavior. Together with a set of sample prompts, these form the constitution. You then train the model to self critique and revise its responses to comply with those principles.<br /><br /></p><p style="text-align: center;"><img alt="" height="300" 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" width="640" /></p><p>In the first stage, you carry out supervised learning, to start your prompt the model in ways that try to get it to generate harmful responses, this process is called <b>red teaming</b>. You then ask the model to critique its own harmful responses according to the constitutional principles and revise them to comply with those rules. Once done, you'll fine-tune the model using the pairs of red team prompts and the revised constitutional responses. You'll build up a data set of many examples like this to create a fine-tuned LLM that has learned how to generate constitutional responses. The second part of the process performs reinforcement learning. This stage is similar to RLHF, except that instead of human feedback, we now use feedback generated by a model. This is sometimes referred to as reinforcement learning from AI feedback or <b>RLAIF</b>. Here you use the fine-tuned model from<br />the previous step to generate a set of responses to your prompt. You then ask the model which of the responses is preferred according to the constitutional principles. The result is a model generated preference dataset that you can use to train a reward model. With this reward model, you can now fine-tune your model further using a reinforcement learning algorithm like PPO. <br /></p><h2 style="text-align: left;">Model optimizations for deployment<br /></h2>To integrate your model into applications, you need to ask how your LLM will function in deployment, how fast do you need your model to generate completions, what compute budget you have, are you willing to trade off model performance for<p>improved inference speed or lower storage, do you intend for your model to interact with external data or other applications, what will the intended application or API interface that your model will be consumed through look like? </p><p>One of the primary ways to improve application performance is to reduce the size of the LLM, which allows quicker loading of the model, which reduces inference latency. However, the challenge is to reduce the size of the model while still maintaining model performance. </p><h3 style="text-align: left;">Distillation</h3><p style="text-align: center;"><img alt="" height="242" 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width="640" /> <br /></p><p></p><div aria-label="toggle video from Model Distillation is a technique that focuses on" class="rc-Phrase css-ugczj4" data-cue-index="44" data-cue="45" role="button"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">Model Distillation has a larger frozen teacher model train a smaller student model. The student model learns to statistically mimic the behavior of the teacher model, either just in the final prediction layer or in the model's hidden layers as well. We focus on the first option here. You freeze the teacher model's weights and use it to generate completions for your training data.At the same time, you generate completions for the training data using your student model. The knowledge distillation between teacher and student model is achieved by minimizing a loss function called the <b>distillation loss</b>. To calculate this loss, distillation uses the probability distribution over tokens that is produced by the teacher model's softmax layer. Now, the teacher model is already fine tuned on the training data. </span></div><div aria-label="toggle video from So the probability distribution likely closely matches the ground truth data and" class="rc-Phrase css-ugczj4" data-cue-index="60" data-cue="61" role="button" tabindex="0"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">So the probability distribution likely closely matches the ground truth data and </span></div><div aria-label="toggle video from won't have much variation in tokens." class="rc-Phrase css-ugczj4" data-cue-index="61" data-cue="62" role="button" tabindex="0"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">won't have much variation in tokens. That's why Distillation applies a little trick adding a temperature parameter to the softmax function. The temperature increases the creativity of the language the model generates. With a temperature parameter greater than one, the probability distribution becomes broader and less strongly peaked. This softer distribution provides you with a set of tokens that </span></div><div aria-label="toggle video from are similar to the ground truth tokens." class="rc-Phrase css-ugczj4" data-cue-index="69" data-cue="70" role="button" tabindex="0"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">are similar to the ground truth tokens. </span></div><div aria-label="toggle video from are similar to the ground truth tokens." class="rc-Phrase css-ugczj4" data-cue-index="69" data-cue="70" role="button" tabindex="0"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">In the context of Distillation, the teacher model's output is often referred to as soft labels and the student model's predictions as soft predictions. In parallel, you train the student model to generate the correct predictions based on your ground truth training data. Here, you don't vary the temperature setting and instead use the standard softmax function. Distillation refers to the student model outputs as the hard predictions and hard labels. The loss between these two is the student loss. </span></div><div aria-label="toggle video from The combined distillation and student losses are used to update the weights" class="rc-Phrase css-ugczj4" data-cue-index="79" data-cue="80" role="button" tabindex="0"><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">The combined distillation and student losses are used to update the weights of the student model via back propagation.The student LLM is used for inference.<br /></span></div><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">In practice, distillation is not as effective for generative decoder models. </span><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">It's typically more effective for encoder only models, </span><span aria-hidden="true" class="cds-336 css-80vnnb cds-338">such as BERT, that have a lot of representation redundancy. </span><p></p><h3 style="text-align: left;">Quantization</h3><p>The quantization technique introduced in Part 1 was Quantization Aware Training (QAT). After a model is trained, you can perform <b>post training quantization</b> (PTQ) to optimize it for deployment. PTQ transforms a model's weights to a lower precision representation, such as 16-bit floating point or 8-bit integer. To reduce the model size and memory footprint, as well as the compute resources needed for model serving, quantization can be applied to just the model weights or to both weights and activation layers. In general, quantization approaches that include the activations can have a higher impact on model performance. Quantization also requires an extra calibration step to statistically capture the dynamic range of the original parameter values. As with other methods, there are tradeoffs because sometimes quantization results in a small percentage reduction in model evaluation metrics. However, that reduction can often be worth the cost savings and performance gains.<br /></p><h3 style="text-align: left;">Pruning</h3><p>At a high level, the goal is to reduce model size for inference by eliminating weights that are not contributing much to overall model performance. These are the weights with values very close to or equal to zero. Note that some pruning methods require full retraining of the model, while others fall into the category of parameter efficient fine tuning, such as LoRA. There are also methods that focus on post-training Pruning. In theory, this reduces the size of the model and improves performance. In practice, however, there may not be much impact on the size and performance if only a small percentage of the model weights are close to zero. <br /></p><h3 style="text-align: left;">Time and effort in the lifecycle</h3><p style="text-align: center;"><img alt="" height="290" 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" width="640" /> <br /></p><h2 style="text-align: left;">Using the LLM in applications</h2><p>Models can provide incorrect answers in situations like these:</p><ul style="text-align: left;"><li><b>Current info:</b> Answering "who is the current president", when the model was trained at a time somebody else was president.</li><li><b>Floating point math: </b>Answering mathematical questions. LLM's aren't meant to do mathematics.</li><li><b>Hallucination:</b> Answering "What is a Martian Dunetree" will make the model "hallucinate" and provide an answer about some tree on Mars.</li></ul><p>These problems need to be overcome via an orchestration library that connects the LLM to external components. </p><p style="text-align: center;"><img alt="" height="268" 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width="640" /> <br /></p><h3 style="text-align: left;">Retrieval Augmented Generation (RAG)</h3><p>RAG is a framework for building LLM powered systems that make use of external data sources. At the heart of this implementation is a model component called the Retriever, which consists of a query encoder and an external data source. The encoder takes the user's input prompt and encodes it into a form that can be used to query the data source. In the <a href="https://arxiv.org/abs/2005.11401" target="_blank">paper</a>, the external data is a vector store,<br />but it could instead be an SQL database, CSV files, or other data storage format. These two components are trained together to find documents within the external data that are most relevant to the input query. The Retriever returns the best single or group of documents from the data source and combines the new information with the original user query. The new expanded prompt is then passed to the language model, which generates a completion that makes use of the data. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="274" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">RAG</td></tr></tbody></table><p style="text-align: center;"></p><p>In addition to overcoming knowledge cutoffs, rag also helps you avoid the problem of the model hallucinating when it doesn't know the answer. RAG architectures can be used to integrate multiple types of external information sources. You can augment large language models with access to local documents, including private wikis and expert systems. Rag can also enable access to the Internet to extract information posted on web pages, for example, Wikipedia. By encoding the user input prompt as a SQL query, RAG can also interact with databases. Another important data storage strategy is a Vector Store, which contains vector representations of text. This is a particularly useful data format for language models, since internally they work with vector representations of language to generate text. Vector stores enable a fast and efficient kind of relevant search based on similarity. </p><p>Implementing RAG is more complicated than simply adding text into the LLM. You need to consider: </p><ul style="text-align: left;"><li><b>The size of the context window: </b>Most text sources are too long to fit into the limited context window of the model, which is still at most just a few thousand tokens. Instead, the external data sources are chopped up into many chunks, each of which will fit in the context window. Packages like <b>LangChain</b> can do this.</li><li><b>Data format:</b> Data must be available in a format that allows for easy retrieval of the most relevant text. Recall that large language models don't work directly with text, but instead create vector representations of each token in an embedding space. These embedding vectors allow the LLM to identify semantically related words through measures such as cosine similarity.</li></ul><p>Rag methods take the small chunks of external data and process them through<br />the LLM, to create embedding vectors for each. These new representations of the data can be stored in structures called <b>vector stores</b>, which allow for fast searching of datasets and efficient identification of semantically related text. <b>Vector databases</b> are a particular implementation of a vector store where each vector is also identified by a key. This can allow, for instance, the text generated by RAG to also include a citation for the document from which it was received. <br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="256" 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style="margin-left: auto; margin-right: auto;" width="320" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Keys (each Text) in a vector database in n-dimensional space<br /></td></tr></tbody></table><h3 style="text-align: left;">Requirements when interacting with external applications</h3><p style="text-align: left;">LLMs can be used to trigger actions when given the ability to interact with APIs. LLMs can also connect to other programming resources. For example, a Python interpreter that can enable models to incorporate accurate calculations into their outputs. It's important to note that prompts and completions are at the very heart of these workflows. The actions that the app will take in response to user requests will be determined by the LLM, which serves as the application's reasoning engine.<br />In order to trigger actions, the completions generated by the LLM must contain certain important information. First, the model needs to be able to generate a set of instructions so that the application knows what actions to take. These instructions need to be understandable and correspond to allowed actions. For example, in a chatbot, that helps users complete an order, the important steps are to check the order id, sending a request to generate a shipping label, verifying the user's email and emailing the shipping label to the user. <b>The completions need to be formatted in a way that the external application can understand</b>. This could be a Python script or an SQL query.<br /></p><h3 style="text-align: left;">Program Aided Language Models (PAL) <br /></h3><p>The strategy behind PAL is to have the LLM generate completions where Chain of Thought (CoT) reasoning steps are accompanied by computer code. This code is then passed to an interpreter to carry out the calculations necessary to solve the problem. You specify the output format for the model by including examples for one or few short inference in the prompt. <br /></p><p style="text-align: center;"><img alt="" height="271" 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" 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NqEHJJIIHHOKu/EXW7jSvh5fXlkJI7y5RLe33DayPKQgPsRuJ+oryXSJf7N0SLV9Y0XW5/CdrqD3tqlrcxfY0BlOxxG2JSATnBOCT05r1H4qRyXXw4ub21DObSSC/CgYJSN1Y/8AjuT+FAHPatqcvg34jeFNMNxql1Zw6M8RtbffM9w65AOwH5m46np6irHjDxxba98KPEd9ost9Y31gyRTRyK0Fxbv5i8HByMjPQ+tO1+W9PxI8O+LNO0i+1TSY9KkLSWSBziTOMAkZPzA4HOPpXP6j4a1/WPCvxA1k6NdW9xr0luLPTmA87ZEw+ZlHQkZOPY0Aem6d4msX1618NN551JtNS+LFfkKZC/ez1z7VWj+Iegf2Rq+pyyzQ22l3jWMxkj+Z5QQMIASWySMVzF/bar4d+JWl+JBoWo6lYyaGunutjGHkilDBvmBIwOgzn+Vc2vgnxHqfgnxAJNInt7//AISU6tFZPLsaaPAyqupxnk4IPUcUAej23ifTvGC33hyS11XSb24s3ZYr6AwStGw270IJ6EjvkUvw11S71fwJYPftuvrUvZ3DE5LPExTJ9yAD+NYHhqHTBr39ty+GvEtgbC0ld7/Wrx3WEY+ZFV5GJyM8j0rV+E9vKvgOG9mjMb6lcz3209Qsjkr+a4P40AdvXGS/E3RrfU0tp7LV4bVrn7INRmsmS283ONu44PXIzjFa/hfxBL4k0ia9fTZbCaK4mtzBMwb5kO3OR2z/ACNeQa1Y+NPE2gvHqlh4jl1iO+SSW3VI47GONZePLA5kOMc8nqe3IB6ZqvxG0nS9av8ASBY6te6hYqjyw2VmZSUZd24EHGAMZzjrxmnzfEbw9H4VsfECSzz21+/lWsEMReeWTJBQIP4gQf8AJFU9C0vULf4s+LtRmtJksrm2tVgnZflkKpggH2NcDpnhvxFo3hjwlqp0O7uJtG1W6mubBF/fGORuHVT1xj9aAOs8MeI7jW/i/qibdTtLVNIjP2G+RozHJvGTsJxkgjkdfWovF0mq6l8XdH8PW2valpdlPprzyfYpApLKXweQR2FWvDcer6h8V9R8QXWh32m2FxpKQwG6C7mIkH3gpIU9Tt64xVXxZ4NHif4vaNLqOlzXWiJprpLKGZEWTLlQWUg55H50AJ4U8T3+i+JvFeiavrZ1fS9Ftlu1vpAPMjGMtG5HBPX8j9Bn/D3xP4iHimy/4SS9kltPFFrJeafE5+W3ZXY+WvsYyD+VaXjTwYtp4WtfCfhDRjaQatexpfXECE+VCDlmdjye3U9M1R8W/D7X7HRrHUtJ17UtXvdEmjnsrKWKMAhSAVXYoP3e3cCgDorPVL9/jfqOlPdzGwj0ZJlti3yLIXA3AeuK7sV5Re3esaR8V5/EkXhXWL+zu9HigAtogWRywYg5I5GMH3r0TQ9Tm1jTEvJ9MvNNkZmU294oWQYPUgcYNAGnRRRQAUUUUAFFFFABRWedRlMkqx2wZUcpkyYzj8Kgu9c/s+0lu7yOC3t4l3SSy3AVVHuSKANeiuL0/wCJmh6nfQ2dvMomnP7gTCSJZv8AcZ0Ab8DXSf2hc/8APmn/AH+/+xoA0KKwdX8TRaFp0moalGkFrGVDSGQnBJAHAUnqRWLD8VfDs9xHALuKN5GCIZvMjUseg3NGB+tAHcUVn/b7n/n0T/v9/wDY0f2hc/8APmn/AH+/+xoA0KKz/wC0Lj/n0T/v9/8AY0fb7n/nzT/v9/8AY0AaHSuO0/wjdW3xM1vxNO9tJZ31rFBFFyXVlC5JBGMcetdD9vuO9on/AH+/+xqrf+IE0yCOa9hjijkmSBCZScu5wo4XuTQBQ+IPhm78W+FW0qxlhhmNxDLumJCgIwJ6A+lHjjwrceJ7Cxawu0tNT027S8s5pFLJvX+FgOdprZ+33P8Az5r/AN/v/rUfb7n/AJ80/wC/3/2NAHI6f4V8Ran4ui8SeKJ9Njns7SS2s7XT97IpcEM7M4BzgkYA9PTnJT4Zaunwp0rwr9rsvttpfi6eTc/llRIz4B25zhvSvQ/7Quf+fNP+/wB/9jR/aF1/z5J/3+/+xoA5jW/C94fG994q82H7GNAlsfLyfM37i+cYxjHvXC+FvDHijxf8LtC0Sa50uHw3MFlllUP9qKLIW8vH3c5H3s/h69/qXxL0DSb+ewv7u2iuYcCWIu7FcjPOEI6Gt+31KSW3ikt7OIxSIGQrLgEEZH8PvQBzV/4X1+1+I8fiTQn01raaxSxuYbwurIqvu3JtByccc1r+N/Dkvijwtc6dbTJBebkmtZnziOVGDKTgE9sfjVu31s3aM9vFBKqO0bMlxkBlOCM7eoPFT/2hc/8APmn/AH+/+xoA851n4U6je+DdE02z1KCHVLUzLfXTE4nSfJnAOMnJ6ZA/Cul1bwdPdeMvCOqWckEVjoaTRvExO8q0YRQuBjjA64rof7Quf+fNP+/3/wBjR/aFz/z5p/3+/wDsaAOU0Xwr4g0Pxtr13BLpsui6zMbiUuXFxG20gKoA24yep7Vlx/DfVo/gm/go3dn/AGgWJEwZvKx53mdcZ6e3Wu1u/EAsZ7OG5gjSS8l8mAeaTvfBbHC8cA9fSrP9oXOcfY4/+/8A/wDY0AZvi7wovivwZNoUlx5EjIhjmVc7JEIIOO4yP1rn4vCvizXte0S78V3WlJaaNL9oij0/eWuZgMB23ABQMZwM966+fVpba2luJrWNIokaR2M33VAyT930FMstcbUbC3vbW3jktriNZYnExG5SMg/d9KAOU0/wFqVp4T8aaU9zatPrt3dT27KW2oJVAUNxnI74zVKT4d6xp48O6lpUmlz6tpumJpt1BfIzW9xGOcggZUg5wcf/AF++/tC5/wCfNP8Av9/9jVWbxAlvqFpYSxRrdXYcwRmU5fYMt/D2BoAb4d07UotMkTX4dINxJKXEVhAViRcAAZblj15wOtZ/jDRdV1OCC007S/D1/p+xhLbamrrhj0ZGUHGOe2eetaWm+IE1exS9sYY5reQsFkEpAOCVPVfUEfhVv7dcf8+if9/v/saAOF034bajp1n4Kt/7Qgm/sK6lnuCxYAh84WMYPAzgZI4/Kreq/DuXW/E/ia7vLmNNP1jTY7NBHkyRupB3EEYwCAevNdf9uuScCzX/AL/f/Y1T0zxFHrNit7YRRz27OyBxKQNykqw5XsQRQBxp8E+Ltbt9G0XxFe6R/YulzRStJZh/Ou/LGEDBhhR64/CteLw34g034l3+vadJpr6XqqQLeJcFxNEIxj5MDBJz3rbsfEUepG5FnHHL9lna2mxMfkkXG5fu9sirn2+4/wCfNf8Av9/9agDnfG/hC71290jWNJltE1bSncxJeIWhmRxhkfHI9j2qo+h+Nrrw7qVrv8P6Ze3uyGNrGNwLeLne27GXcg8DAA9a6K98QJpwtzdxRxfaJ1t4syn55G+6v3e+Kt/brj/nzT/v9/8AWoA4/Q/h9deEtYuU8O3qQaJe2QinhkZjJFcKu1Zk4wSepBxz+FcxZ/CjXRaaVBcJoEU9hqMV3LfoJJLm92uSS7sMrwenOTjpXq3264/580/7/f8A1qPt1x/z5r/3+/8AsaAOXt/Bl/DdeOpTPbFfEC4tgC37v90yfPxxye2aqnwFqTeHPA+nfabUTaBdwz3LZba4QEEJx1574rsvt9x/z5p/3+/+xo/tC5H/AC5p/wB/v/saAPJ9Dh8VXPin4g2/ht9KRZtSMc0l75gaLKkbk25BwM8HviuksPhvNpF74H+xXUL2nh9bj7QXyrzNKvJUAEfeJPJ6etbGq+PNH0G7a2v3ghuNnmyIjM7Iv959qHaPc4rZt9We6tori3hhlglUPHIlxuV1PQggcigDhLXwP4m8O3F9Z6CPD11pd1cvcRS6nExmtS5yVwow4HbJFel26NHbRRuULqgDFF2qSB2HYe1Uvt9wP+XRf+/3/wBaj7fcf8+a/wDf7/61AGjS1nfb7j/n0X/v9/8AWo+33H/Pov8A3+/+tQBo0Vnfb7j/AJ9E/wC/3/1qPt9x/wA+i/8Af7/61AGjRWd9vuP+fNf+/wB/9jSfb7j/AJ81/wC/3/2NAGlRWb9vuP8AnzX/AL/f/Y0fb7j/AJ81/wC/3/2NAGjRWf8Ab7j/AJ81/wC/3/1qPt9x/wA+a/8Af7/61AGhRUdvL59tFNt2+YgbbnOMjNSUAFFFFABRRRQAUUUUAFB9qKKAEpR3oooATvUdxbRXVrNbToHhmRo5EP8AEpGCPyqWigCppumWmj6bb6dYQiG0t0CRRhiQq+mTzVqlooAKQ8+tLRQAD0pMc5paKAADFRR2sEM808cMaSzkGV1XBcgYGT3wBipaWgBO1FQX13BYWFxe3LbILeNpZG9FUZJ/IV5lafF2aRLHU7vTdPh0a9uFhUJqKvdwqxwsjxAdPUA5FAHqgFGK85vvifNpF1q2kahpP/E/t5o49OsoZSRfrIcI6kjgdd3pin6r8Qr60146BZ2elnUra2Se+kvdQEEEbsMiNCQS598CgD0LANKRmvOR8VUvNC0abStKa61nVbmS0hsDOoWOSP8A1haQZBUcHIHINW9W8ZeIPD2hRXms6DaRXEmow2aCG88yORJOrg7QRjpgigDuxSE7ASfujqc9K5fU/FVxZeOrHw1b2UczXdhNdLK8pXDpnCkYPBI69vSuM8Ea7r+p6B4tm1nT4bjThc33ms16xZSFH7hV2/cxwGBH0oA9ZiljmjWSKRJI2GVZGyCPY96cODXluheM7fR/A/hHT/D+hqb/AFhXWy083J2RKGJdnkYE4HXpnmtJfiJfWaeIrLWdHitda0fT21BYop98NzEAeVbAI5wDkUAeg9aiubiCztpbm5lSKCJC8kkjbVRRySSegrzmD4l6yi+HtR1Hw3Ha6Nrc0VvFKt4HlR5BlWKYwFPPfOOuDxV/4rO9x4f0vRFYqms6tb2UuDjMZYsw/JaAO6jdJY1ljcOjgMrKcgg9CKbM8MUZM7xpGfl/eEAHPGOfWuM1Xxfqll4zPhHQ9Cgu5109LqJ5LnyY413FSG4PAwMYyeew5rl/EPjKz8SeAJ313w6r3VjrMVjd2DXLKqShsBldeSOf50AdhD8MfB9veLcx6Mo2v5iwGeQwq2c5ERbZ+ldPeTWiweXeyQrDMfJ2zMAJC3G3nqTzxXG6h401s+NNS8L6HoEF5cWlvHcCae78pArDkH5Sc5IAx75xXJeKvFsXivwH4a1eSAWTx+JLeK5idwVidGYN83cY5zQB65GlhomliNBDZ2FpFgZIRIkUfkABViN0mjSSNw8bgMrKcgg9CDXJ6R4hXxxd6xZW+npL4bjV7Rr6Rv8Aj7kPDiNcYKAHG7PPaq/wouZpfAdvZzy+ZLptxNYb/VY3IX/x3aKAO4pDRzRQBBdWlvfWc1pdQpNbzoY5Y3GVdSMEH8KfDBHbwRwQoscUShERRgKoGAB7cVJRQBDb2sFnAlvawpDCn3EjXAXv0qXFLRQAgA6UpoooATHNL9KKWgBKMCiigAAwc0UUUAFFFFABRRRQAUUUUAY8P3rj/rvJ/OuR8Zol34l8G6ddKG0+fUJHmRvuu6RFo1Pr83OPYV10P37j/rvJ/OqWu6DZeIdNNne+YoV1lhlhbZJDIvKujdmBoAzvH9ta3ngLWluwAkFo88b94pFBZGX0O4DpVjRl1C+stE1O4vZI91ghubTywRJIyqd5bqCOePeuX1X4Y3OtzGfUvFN3czKgRM20aLIFbcqzBceaoI6cdTXaRWd79ps7me/IaKAxzW8MYWGVzj58HJGMHAz3oA5z4pl18DStEm+UXlqVXdjcfPTAz2rF8X6x4h1qKPwrq2hWejRayTAt9cXv2iNSCDtUKv8ArCB8oJFdx4h0OLxFpP8AZ880kKGaKXegBOUcOBz6laXxFoVn4m0W50y93qkuGSSM4eJwcq6nsQaAMCaTUtX8X3Ph+01e506x0mygeV7dE86eSTO35mBAUBew5J61lN4l11IX8PNeodVGtJpS6n5S/wCqaPzfMKfd8wLkY6ZrobnwpdPe2+p2Wu3NpqyWy2txciFJFukXoXjPG4EkgjGMmsvWfDNno/hVo3j1TUJn1Bb24v7bBu45s/8AHwqgc7eBsA+7xQASXWr+GvF0Frc61calpR0q6vfLuI4xLvi2dWUDI5447nrxjmrfxjrcmg22uw3+u3mqyBJ20xNGf7G8bEZjRgmeFPD7jkj0NbOgWM2r+PhqrXWp6lZQ6ZLbS3V/a/Z43Z2XEcce1eMBixx1PWtWDwJPBZxaSPEuo/2DC4aOxCqHCg7hGZh82wHt1xxmgCG1k1vWfH2tWset3FlpmmNayJbxwoWkLpuZWJGQvB98n2qb4mCd/DFoLd1juDqtn5bsu5VbzVwSO4z2rfsdFhsdb1bVEmkaTUTEXjIG1PLXaMfUUa5osWu2cFtNM8Sw3UN0CgBJMbhgOexIoA5+2fVtE8dWmlXWtz6nZ39jPORdxxqYpIivKlAMKQ3TnFcpf+K7+00waraeKb7U72K4TzVttP8A+JaVMgUoHKDgA43biSfrXo+o+H7bU9ZttSmkkDQWs9r5QxtdJdu4k9QRtrnpPh5PceH00C58TX0mlW4QWsCwRIybCCgdgMuBgccdKAKPirV9T0/xDqK6hqer6PpiRp/Z13ZWazW+dvzNMdrHO7jHAxXc6RcG80eyuGu4LtpIUZri3GI5TjllHYH0rHvvDN/Jqd5e6b4iu9PF8qrdQiFJ0JC7dyb/ALhx6ZHfFa2jaTa6Do1npViGFtaRiNN5yxHck+pOT+NAHGx6Z4y0KLX9VtLjRnEt7PffZXid2nQAYXzMgIdq4AwcGux0fUU1zQbLUYA8KX1usqZ+8m5c/TIzXPS+BZVS+srHxBe2ej30jyXFikSNjecuqSEbkVueOepxiurtbWCxtILW1jWKC3RY4o16KoGAB+FAHkum6nqHhfwFczW2oXU9zf69LYxM8Cy+QTM++VURQXYgE7eRnoAOK39A1zUF8UWthb3Wu6nptzBJ502p6a0BtpVXKsH2KCrcjaehxg1pDwHA1jqNhLql09nc3n261VURHsp95cujgZPzHv8ASr2neHb6LVU1LVtfutUuIomhgUxLDHGG6sVThmOOp/KgDiINU8TR/De08aSeI55LhNjtZ+RGIJI/N2EN8u7cRzuBHPQCugf+29d8ea9pUOvXOnadZRWrx/ZoozJvkUn7zA8cZIxzxyK0h4Mtf+EDTwmbuf7MqBPPAG8gSb+nTrxWnY6LDY+INT1hZXeW/WFXQgYXylIGPrnvQBx2l+JNXudN8JG6ug09xrM9hdyJGo89IxKAcdidik4x0qhN4h1Wz1KVdc13UdDvxfFYEubJW06SHfhVEiqTyn8RYEGuutPBtraQ6VEt3Mw07UZdQQso+Z5C+VPsPMP5VSufAk9za3OlP4ivjoNzKZJLBo0ZsFtxQSkbgue3XHegDofEBH/CM6se32Kf/wBFtXntgdc8MfDvQfEI1ya4SGG187T3iQQNA+xNq4G4OAwO7Jyc8V6Vd2iXenXFiSUjmhaEleqhlK8fnXKWngF4bPT9Lu9fvb3RbBo3isZYkG5kwVDuBlkBGQvsPSgClruqSv4g1K0/4SjUYZIdq29lotl57QnbnMx2MNxPbIGKraLrF1r+qfDfVL3abq4tr1pSq4BYIATjtnGa6A+ELiHUtSm0/wAQXllZ6pMZ7y2jhjLGQgBikhGUyAPX2xT9J8F2mjjw+IrueQaIk6QBlHziXrux6dsUAcbpN5q9p8PfDr2X9oJp5uboahNpsCzXCJ5r7NqkH5SepAJGK7LwVqi6np10Y9e/tiOK4KpJLB5U8S4+5KuB8wOecDIqGLwQLPSdLtNN1m9s7nTZZXhuVVWDiRiWV4z8rDn6jGc1Ys/Cstpaas41q6OraoyPPqIjRWUqMKETG0ADjv1NAHTL95frXlfw+ufFqeE0XStM0iezF3c7JLm8kjcnznzlQhA5969STKquTkrjk96yfDmgw+G9FXTIJpJo1lkl3yAA5dyxHH1oA83sPEOp6D4V1+cRxw6peeKHtCYEa4WBn27mRcZfABwMcnHFb3h/W79PFVnYQ3Wu6npt1FILiXU9NaA20ijKsH8tRtbkbT0OK2X8D2Eul6pYyXNzi+1FtSWZCFkt5sgqUPsR3qfTvD19Dq8Wpatr91qc0ETRQRmJIY03YyxVPvNx1P5UAZvxHiup9P0KKxlSG6fWrZYpXXcEb5vmI7464qOS81Xw74mTSJtYuNSttQ065uIXuUTzYJoQCeVABUg9COCK3vEnh6PxJZW9tLd3Fp5F0l0ktvgOHTOME9OTn8Krab4Wkg1WXVNX1WfVr1rdrWNpIliSKJj8wVF4y2Blupx2oAxLjX9TT4U6Fq63TC/ufsHmzbR83mSIH4xjkE9u9T3fii58Oal4sg1OYzJaQDUdODAAtGw2eUMDnEgA9fmFNT4dOdNtdKuPEd/NpdjLHJaW3lRqU2MGUOwGXAxgZx+gqLxFYW/inx9o1qlrdj+y3ea+nMTJC0fyOkW4jD5dUOBnG00AVNTvfEemJo8Ws32sw2J08Nd32lWiSv8AayckOAp2oB0wOe9dZ4S1A6l4btbltXg1dm3D7ZDH5YkAJxuX+FgMAjA57U3VdAu7rWI9W0zW7rTL0Q+RIBGs0UiZyMo3AYEn5hg1Y8PaDB4d01rSGWWd5ZnuJ55cbpZXOWYgAAc9hQBl6tf6Z4TnuTY2bXWu61Jujs4yWe5kC7QWz92MAcngAZq94P0WXw54S0/SZ5FkmgQmRk+6GZixC/7ILED2FZM3ge8PiK/1u08U6haXN7hX2W8L7EX7qKXUkKPQdTzXT6baz2VjHBdX0t9Mud1xKiqz89woA9uPSgDz2W88R6lB4zvYvEU9mmi3k62kMMEZDbIw+HJUkryBgY78nirdtf69bXPhHVLnWZLhddnSK5sfJQQRB4mkHl8bhgjqSc10cPha3gsfEFot1KV1qaWaViBmMyIEIX6Ad6dJ4YgktfD0BuZQNEljlhbA/eFIygDfgc8UAc5psviDxTo194htfEE1gwmnWwtI4YzCqRMVHm7lLMWKnPIxniq8Gta74u1jQrex1WTSLa/0MahcGGJHZZPMC4UsPU/kPU5rZm8ETD7da6d4hvtP0u/keS4s4oo22l/v+VIw3RhsnIGcZOMVqWnhiysNZs9QtS0S2mnf2dFbj7qxhwwOeueMUAcNL4nvtS1bWkfWtcsEsLhrOzTT9Ka4R2jABklYRsGJbPyAjArvPC+p3useGNPvtRtHtL6WP9/C6FCrgkE7W5AOMjPY1nXHhG5jv7+40XX7rSY9RfzLuGOFJQ0hABdC3KMQBnr0Bre02wh0rTbaxgaRo4IxGrSuXdvdiepNAFuko60YoAKKKKACjsaKOxoAtWH/ACD7b/rkv8hVmq1gP+Jfbf8AXJf5CrNABRRRQAUUUc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lAFXUrCLVNLu9Pnz5N1C8LkddrAg/zrzfQPBfiXRY7HSW0jwncWlpIB/acsJM7wg55Tb9/HGd2PrXqX4UfhQB5brHw61zXr/UfEdzfQweI4rhG0by5WMVrFGchGOOd+SW4+npU2peC9cTxRJ4ls9L0HUJ9Rtokv7C/wAlI5UAAeJ9p4+oH+HpeD0xRg+lAHnOp+Ctens/D+rWSaJaeIdHuJJhb28bR2siScMnqDgAbsevSpfEfhzxb4u8JzW9+dKs9ShvIruxigd3QbP4ZHI5JyeQK9Cx7UfgaAPPrDw74qvfiHpfijXF0qCO2s5bY29pI7Mu7ocsPmySfTGB1qHw94S8S6NF4l0Zhpsmk6jJdXFvceawm3yrhVK4xgdzmvSPwNJz6GgDy2H4da3p2heD7mwuLL+3vDyyIY5WbyJ0fO5dwGRweDjuall8C+INak8TaxrDWEWq6npTaZZ21u7NFAhHVnIySWweB616b+FABz0NAHnmo+B9Wu/BngzSI5LYXOi3VpNckuQpWJcNtOOT6dPwq78VLWd/CkGq2sRlm0a+g1EIoyWWNvmA/wCAkn8K7b8DQRntQB5Hf3ery/G03nhyGyuJpPDscgivXaMPGZcjBAOCODyPWpJ/hnrc/g69t5rmzk1vUtZTU7shmWJMNnYpxk4HqO9ereVH53neUnm7du/aN2PTPpTvwNAHJaf4ZvrX4o6x4kkaH7DeWMVvGoY7wykZyMYxx61yMfww1g+C7PRJ2sJJIvEI1KVWclGgySV5XkkHoRj3r1vHtQM+hoA4LTdHvPh5fa/d27RN4SaJ75LYM3mWkoBLqi4wUbHTPHp63fhlptzpvgOx+2qVu7tpL2ZSOVaVy+D7gEV2GOMY4ox7UAFFH4Uc+lABRRz6Uc+lABRRz6Uc+lABRRz6Uc+lABRR+FHPpQAUUfhRz6UAFFHPpRz6UAFFHPpRz6UAFFHPpRz6UAFFHPpRQBjw/fuP+u8n86lHJwKih+/cf9d5P51yvjENfa94Z0KeWRNO1Cec3KxuU87y49yRlgQQCeSM84oA7DB44pP5etef6za6H4a07WLK21PUbZLn7ODpthIDJEzttURbvueZ0PIHUjFUNKs5NF+Jnh6K30E6DFfQ3KS24v8AzzcBEBUuoyAQe+STk0AeoYNIRivGINCtn+D974kkkum1m3FxPbXZuX3QbJm2qozgDjkY5ya3vEGlXmo+J7i8OmQ+JbX7HAj2CX3kz2LEEllXIUl+oPB44oA9Kx9aADkcfSsLwZfWupeEtOnsZryWAIYw16QZgVYqQ57kEEZ9q5XRdD0/xZp2vavrckz36311CswuGQ2CRMQgTBwhAG7PfPNAHV694jfSL3TrC30y41G/v2k8mCJ1TCoAXYsxwMAj61d1fVbbQ9HudUvt629uoaTYu48kDgfUivMdMs4fEerfDzVNYt/OvbuxuGnkZ2BlMar5bEAj/e4655rqvirawXfw61Uzx7/KCSJyflbeozwfQkfjQB2Q5GeR657UYPPFeX6vZPN45h8OW+iHU9L07S0mg043/kIWZyDI27mTGAMHpmqdxFqcHhK502eZ7SJPENpDbRRagJ5bSN3TMZkHIwScA8gEUAeu9Dg5o2t6GuFg0Wx8NfEfTrXSkkt7a/026NzD5zusjoybXIYn5vmPNcfBo9tB8HbTxSst0dcgCTRXpuXLIBPt2AZxt28YxQB7BBdSzajdWzWc8cUIQpcPjZNuBJC9/lxg/WrOMV5zq63NzrXj61i1ZNPdrWwWK4lmMaISrcZ/h3fdyOead4ZSHRfF1tY3fh+60G+u7eRIo4Lz7RZ3ezBLcnIcDuQDgnOaAOx1fW4dGm0yOaKSQ6heJZx7MfKzBiCfb5e1ahBB6VxXxEtIb4eGLS4neCKbW4UZ0co33H4DDkE9PxqhPHYeDfGVzb2E8tjpc2iT3l5FGxcW7RsAsqKc4YgkehIFAHomD6fWuavvGlnYR6u72s7f2beQWUgBHztLswR7DeM/Q155cWp0+y8O6vZaDd6a0mo2irql7qO66u1kYbg8YJB3Dkgnj9Kv+IxiHxsP+pg07/2hQB6zg+hPakBBrzHxHpE8/iHXb+bSj4isywUSWeoeXdaZtQZVYycZ/j4wTnmu+0G8t9R8P6fe2ks01vPbo8ck/wDrHGOre/r70AR+JNdh8NaDc6tPBJPHBtzHERuYswUYzx1IrLj8Ta40qK3gfV0ViAWNxb4Uev36rfFQF/hzqYQ4O6HBx0PnJzV230PxhHdRPceLY5oVcGSIaTGu9c8jdu4yO9AHS/40mDnGDn0rzzWbWz17xfqsEehXuvyWqxQSi4vBb2tmSu7Cc5LEEMTgketYfh+7utd8N+B9I1C5maxvbq9juSszZnWEv5cRcEEg4H1C0Ael+Idbh8O6Q2ozwySxiWKLZHjJLuEB592rVKke/vXlnjnRtM0nwxrOm6ZfywmaewkbT1lLLa5nUB0zkru9M444Fa76LZeGfiL4dTSRNAuoRXcV2pndxPsRWVm3E5bOeevNAHd0pBFcl4+mlGm6VZLcSW9vqGqW9ndSxOUbymJ3AMORnAXI9ap6pYeHvBq6lPBqlzpKSaa7SWds+84DAeeitnDgsFz0OeaAO4wfQ8UYPpXkRsZNH1zwld2nh+40Xz9Rit2nn1DzJ7uN1YsJEBIOeCSTwau2nhyw1y28a3uo/aJp7bUrtLU/aHAt9qBgUAOAc859hQB6gOvNZuh6xFrtlNcwRPGsV1Na4fqWjcqSMduKg8HXk+o+DdCvLpzJPNYwvI56sxUZJ/GvOrXwzp914F8VaxMbg6hBeajLazCd1Ns0bsV2AHA5GT65oA9eo5zjBzXm19LF4gvtIt5NMv8AX75dIhuZbIXCwWsRkA/euSRlycgDnA7CszTLW+v/AAbqekwXVvbtb+ImghsZr9vLljXaxtRMPmwefyxQB67g1j634ij0eaytEtLi+v71mW3tLfaGYKMsxLEBVA6knuK5zwY1tY+Ib/TG0a+0O+ktkuG097kT2xQMV8yIg8Ek4PTtxXV3t5bm7i0tbxLfUrqGRrUlNzYXG5lB4OMjigCvoPiCDXVu41t57S8spfJurS4A3xMRkdCQQQcgg81ZudXtbXWLDSpN/wBqvlleEBeMRgFsnt1Fct4Ljn0zxT4m0e8nW+vVeG7l1HGHmDqQqOo4UqFwAMDB6U3xFo9jefFXwjNcQb5HhuiTvYcoFZOh7En6980Ad16cHnnpQAcE46V4paW2qeIdC1TWrjQ5Li/ee52as2sC3NkUZgoVD/q1TAyO/frXRJYf8JH4z0a21x/PVvDiz3MEE7CGaXzQMnaQGXJJHbpQB6SOexpDkAkqfpXkN1FLb+GdZ0W2u7mG2tPFVvaWrLKd8ETNGdqsecAscV0sWj2Xhv4k6ZbaSktvb3+nXRuYvOd1laMoVchifm+Y80Adhp9zLe6fFczWc1lI4y1vPjfHzjnBIqzXkOmWN9q/g3we0SW+reTb3Ek2jXN60LXI34EgP8WzphuPmrs/Ac9r/Zt9Y29vqFlJZ3ZSaxvZRIbZmUNsRgTlMEEc96ANmDWYbjxFeaKsUgntLeK4eQ42srlgAPf5TWlg5xg1wMuhadr3xW1ePUVaaOLS7UrbGRgjktINzAEZx29N1Y8F1EfD82h3MuqX0EXiGWwsLe1mxJdxoN3lPIx/1a5IJznCgUAen31ythp9zeOpZIInlIXqQoJOPyrI07xRbaje6RbR28yNqenHUIyxGEQFRtPv84/KuEtIJNL8S+IbBNLXSIJvDc1w9hHeeeocEqHPZSQSMCrWjZF74Txci1P/AAh8uLg9Ij+6+b8Ov4UAen8gZNFeTaJaL4fv9Al1jSbi2mkmSCPXNO1Azw37yDjzlY7sOeehwcYIr1k54oAKD0NFB6GgC3Yf8g62/wCuSfyFWKr2H/IOtv8Arkn8hVigAooooA4yaGKS8umeMM32iTk/7xpn2WD/AJ5J+VTP/wAfVz/13k/9CNFdEUrDIvssH/PJPyo+ywf88k/KpaKqyAi+ywf88k/Kj7LB/wA8k/KpaKLICL7LB/zyT8qPssH/ADyT8qloosgIvssH/PJPyo+ywf8APJPyqWlosgIfssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyT8qPssH/PJPyqWiiyAi+ywf8APJPyo+ywf88k/KpaKLICL7LB/wA8k/Kj7LB/zyT8qloosgIvssH/ADyX8qRraDY2I1zj0qamv90/Q/ypWQHUabzpVn/1wT/0EUUaX/yCbP8A64J/6CKK52Iow/fuP+u8n86q6toun65Zi11G386MOJEIYq0bjoysCCrD1FWofv3H/XeT+dY3iTXLjRrzQIYIo3GpaklnJvz8qsrHIx3+UUgEj8FeH00u709rAyw3jK9w80zvLIy/dYyE7sjsc8dqdY+D9D0+/t9QhtZZL63J8u6uLiSWUAggjczEkYJ46U7xbrNz4f0mC7t4Y3d72C2IlBwFkcKTx3was6r4h0TQ5kh1TVbOykkBZEnmCkj1x6e/SgBq+HdKHh+TQhagabIrq8AZuQzFm5znkk96r6n4R0fV7tLu4hnjuViEJltbmSBnjHRWKEbh9a001CykuIbdLuB5pojPEiuCXjyBuX1HI596Yusaa9td3KX9s0FmzJcyCQbYWXlg57Ed80ASWNja6ZZQWVjbpb20C7IooxgKKx9Q8E+H9TvZru5spPMuMG4SK4kjjuMf89EVgr/iK25LmCK1a6eVFt1QyNKT8oXGck+mKzYvFfh2a6e1i1zTnnjjMroLhchAMk9egHNAFptIsWvbG7+zIs1gjx2pXIEasApAA4xgAfhT9Q0601bTrjT7+BZ7W4TZLG2QGH4c1FpuuaTrNtLcaZqVrdwRHEkkMoYIevPpxzzVew8U+H9VvPsdhrVjc3OCRFFMGZgOpHr+FAFa58G6Jc2dlbyQXGbJSlvcJdypOinqPMDbiD6E1PB4V0S20uLTYbFVtY7hboLvYs0ysGDsxOWbIBySaS78W+HLGURXeuafBIZDHsknUHcDgj2weK00vbV71rJbiI3SxiVoQ43hCcBsehIPNADJdNtJtUt9ReENd28bxRSZPyq+Nwx052j8qqDw3pH/AAjo0H7GP7MC7RBvbGN27rnPXnrV+3vLa5knS3uIpXt38qZUYExvgHafQ4IOPesPTvGGnal4u1Pw9HNAJ7JY9uJQWlYglwF/2MAH60AaFx4f0m6k1F7iyjl/tKNIrsOSRKqAhQR2xntVbSvCWjaNefa7SGd7kRmJJbm5knaND1VS7HaPpU1n4m0HUNQbT7PWLGe7XOYY5lZuOuB3x3x0pl94r8PaZIY7/W7C2cSGIrLOAQwwSD9AR+YoAsavoem69bRW+p2iXMMUglRHJADgEA8d8E1V07wno2mR3aRWrSm8Ty7iS6led5UxjazOSduD06Vbvdc0nTo0kvdStLdJIzMjSzKoZBjLDnkfMPzFUrnxDa3Phu51XRdT0yZI+FuJpv3CncAQ5HI6/nigCtD4A8NwpEhsppVhZWgE91LIICpBHl7mOzGB0xwMdK0bjw5pN2LwT2YYXs8dzcfO3zyR7djdeMbV6elGpeJND0aZIdT1eys5XXcEmmCnHrjsPetON0lVHR1ZHAZWU5BHqDQBg6l4L0LVr+a9uraYTXChbjyLmSFbgDgCRUYB+OOe1aA0XTkubG4jtljksImhtghKrGjAAqFHGMAdRxisDR/HmmTvew6vqOn2FzFqU9pDE8wUuiNtVjk8Z9eldBqmt6XokSS6pqFtZpIcIZpAu8+g9fwoAXVdJstc02bTtRgE9pMAJIixXdggjkHPUCsAfDXwoHDCwuAQcg/bp+v/AH3VjW/FcNt4ct9Y0ea1v4ZryC3Dq+5CHkVG5B6jNS2PiISap4lhvnt7az0e4jjE7ttG1o1clyeOpoAW98G6Ff6nLqE1rKJ5wq3Hk3MkS3AUYAkVWAfA45FPfwhoDaKujjTo1sEmM8USMyGKQkncjA5Q5JxgjFW9L17SdbWQ6XqVreeUQJBDIGK56ZHUZqe/1Cy0uze71C7htbZMbpZnCqCegye9AGVD4M0GDT7iy+xNLFcyxzTvLM7ySuhBQtIxLHBAwM1p3OnWl3qFpfzQhrqz3+RJuPybxhuOhyPWsjSvFMOs+KLywsZrW40+GwiukuIm3EszupBPttq5Y+J9C1O+axsNYsbm6GSYop1Zjjrj1x3xQBc1LTbLWNPlsNRto7m0mGHjccHByD7EHnIrLtPBmg2kN3F9ie4F5F5Nw93O87vH/c3OSQvsMVZv/E2haVeLZ6jrFja3LAERTTqrYPQkdgfepdT1zStFijk1PUba0SU4jM0gXf349fwoAzbfwL4ftpLeUWs80ttIslvJcXcsrQlegQsx2j2HB71qwaRYW0V7FDb7I76V5rkBifMdxhj7ZHpU1ne2uo2kd3ZXMVxbyDKSxOGVh7EVzj+NbHT/ABJrOn6xeWdjbWf2fyJJZNpkMiFm6nnGO1AHRWFlBpthb2NpH5dvbxrFEgJO1VGAOarxaFpsWm3enJbAWl40rXEe4/OZCS/OcjOT0qSfV9NttMGpz6hax2BUOLlpQIyp6EN0OaTTNY03Wrdp9Mvre7iVtrNC4ba3ofQ/WgDPvfBuiX89tO9tNFNbQLbRy21zJC3lDojFGBZfY0g8F+HU0u50yPTIo7O5mE8kSMy4kAADqQcq3A5GDTNe8Sroev6La3MltDYXi3DXE8zbRH5aBhyTjkmtPT9a0vVrR7vTtQtrq2QkPLFKCqkdcnt+NAFfSPDWmaHLNPZxytcTgLJPcTvNKyjou9yTgZ6dKl1jQtO12GKPUIGcwv5kMscjRyRN0yrqQR+BqLTvFWgaveGz07WbG6udpbyopgWIHUgdx9KuJqVhLpzail7btYorM1wJB5ahc7iT04wc+mKAINH0LTtBtpYdPgMYlfzJZHdpJJW/vO7Elj9TTdV8P6ZrVxY3F7AzT2Mnm28qStG0Z4zypGQcDIPBpL/xLoelJA+oatZ2q3C74fNlC+Yv94e3vWlDNFcwJNBIkkUihkdGDKwPQgjgigDnr7wN4e1G9nuriyk3XLiS5ijuJI4Z29ZI1YKx+o5rXXS7JNTXUVt0W6S3+yq68BYt27aB0xmql/4q8P6VI6X+tWNs6P5bLLOqkNgHBH0IP4irV3rGmafp41C8v7aCyIBE8kgCMD0we+e2OtAFd/DekyLcK9oCLi7S+l+dvmnXG1+vbavHTirU2nWs+pW+oyQg3dvHJFFJk5VXxuGOnOB+VQweINGurBb631S0ltGlEImSUFfMJwEJ7MSQMVZN9af2gLA3MQvDH5wt93zlM43Y64zxmgDJn8GaFcabY2H2NoorAsbV4J3jkh3Z3bXUhgDnkZq/pOi6foVobbToDEjuZJCzl3kc9WZmJLE46k1l6rrepT3NnZeGYba6eaSWOe+l3Pb2nl/eDbSMvk4C5HINJ4e1vUrnWdU0PWIrb7dYLFL59pkRSxyA7TtYkq3ynIzQBZ1PwhourX7ahdW0ovmRY/tMFxJDIEGcKGQggcnI79+lE/hHQp9GtdJNgIrO0YPbiB2jeJx/ErqQwbk5OcnPNbdFAGFaeDtAsppZobJjPNbvbTTSTO8ksbY3B2ZiW6Dk9O1W4fD+l28tpJHaKDaWhsoQSSFgOMpgnkfKOvNaVFAHPWPgfw/p17BdW9pLutmL28UtzJJFAx7xxsxVTzxgcdq1rXTrSyuLue2iKSXkvnTsWJ3vgDPPTgDgVbooAKD0NFB6GgC3Yf8AIOtv+uSfyFWKr2H/ACDrb/rkn8hVigAooooA5B/+Pq6/67yf+hGkok/4+rn/AK+JP/QjWL4t1Z9F8M3lzB/x9uogtR6zOdqfqc/hXQthmyGVhlSCPY5pa4jwfbp4Y1268K/aBNDJbx3to+7dlsBJhn/eG7/gRoHi7XrnT9V1Kx0exNnpdxPFMJrhhJMIic7ABgHHPPencZ29GK5G/wDF0/8AadhYacunwPeWSXiTanOY0YMeI12j5m7n096vW+v363+i2upaYLRtSE0bfvQ/lyoMgAjgqygkd+KLoR0GKK4u28fCfR9fvvsQD6dIFtY93/HyrHbE3tuYEVPdeJ9Rl1e603To9KR7AILqW/ujGrSMu7y4wBnjjLH16UcyA62iuNbxrc3Om6LNpmnRy3epzy2phmmwsMsYOcsByoKk5HUdK6Gyl1lrmBL60tEi+z5meGYttmz91QRyuOcnnNO6GaNFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFI33T9D/KlprfdP0P8qAOo0v8A5BNl/wBcE/8AQRRRpf8AyCbL/rgn/oIorlZJRh+/cf8AXeT+dcX8SbOHUJvCVpP5ghl1yNGMcjRtgxv0ZeQfcV2kP37j/rvJ/OnNFHIyNIiuUbcm4Z2n1HvQB5p418HaTo2k2N7aNf8AnLqloB59/NKvMq/wsxFaem32kaX438XjXp7S2u5po5I2vWVfMtPKUAIW4Khg4IHeu5kijlULIiuoIYBgDyOhqG6sLO+KG7tLe48s5TzolfafUZHFAHk1nMPDvhDRPFwikSy0/UbsRIVIP2GdmCDB527vLI+tV7fS7rT7i18JTLIT4pS0vLk4JAYFnu8n3CqPxr2d445YzHJGroRgqygg/hSGKNpVlMamRAQrYGVB64NAGX4p+Xwhre35cWE+Pb92a4a80uw/4R34ZW/2SDyvt1rkGMc5gZjn1yRk+tensqupVlDKRggjIIpphhIjBiQiMgoCo+Ujpj0oA838QXNrpOveOrqXTlu4Boto81quUEwLSKdxHIGMZPoKzb2/kk8QeCFk1nQrnbqURjttIg+WCMoRzJuJxyAAQM/hXrflRb2fy03uNrNtGWHofaoLfTbC0Qx21jawoXDlY4VUbvXgdfegDgdIsLOTwp4/me2heWa/1ESMyAk7V+UEnsKr3F4vhvw94M8ZuGMFvp0dlf4BJeGSJShP0kC/99V6asUSq6rGgVySwCjDE9SfXNYHiDw/da7JZ2Ru4IdDR0kurVYMvNsbcqBs4VCQuRjtQAeCtMn03wtbfbB/p94zXt4f+m0p3MPwyF/CuT1d2HiD4h2Nm6Jq1zpcDWkSkLLJiF9xQdSee3qK9OJySaha2t2ukumgiNwilUlKDeoPUA9QKAPIIHivdE8L2sfifRDGt1bNY21hph+1RupBIIEpK8ZDkjuc11vh2ws59W8eyS2sMry6i8Tl1DEp5CHb9Mk/nXXx6fZQ3b3cVlbR3L/fmSFQ7fVgMmplijQuVjVS53NgY3H1PrQB5P4btobu9+GH2mJJRHo906CQAgMFTB59KPFMccNj8T440VIybF9qjAyVXJx6mvVkt4E8vZDGvlgqmFA2g9h6UNbwOJA0MbCTG/Kg78dM+tAHnWuPDaeLtZl0/X9LsL6S1hN9Za3APIuUCkK0b5DYxkHGRntXXeC71NR8HaTdx2AsI5LddlqudsY6DbnnbgZHsRWldafY3xja7sra4MZyhmiV9v0yOKsjjGOMelAHlmk6l4VtPD/jWHVJbOOaTUb37TFNjzJhkhNoPLegx/FnvTbPULyxu/DmnXX9lafrEGgxvLqWs5YgEgGJF3KCwwCxzmvTH06xlmimksrZ5YmLRu0SlkJ6kHHBpbqxs75UW8tILhUO5RNErhT6jI4oA8dt5Uk8L+IWS5iuVPiu0YSxQ+Uj5aH5lTJwD1Hr171t6tDYTSfEBdTuZrW2Gq2bfaYohJ5LCOMq7KeCgbGc8Yr0o20DbswRncQx+QckdD+HaneVEfM/dJ+8/wBZ8o+ftz60AcH4b1O4fxybG8m0PV7ltPLjVdLTY6IGGI5QCVGScjB7dKu+MXgtfE/hO91Iqukw3MwlkkI8uKdo8RM2eBzuAJ6E11VnYWWnoyWVnb2yMcsIIlQE++BUssMU8TxTRpLE4wyOoZWHoQetAHk+tSQ6nrPj/wD4Rp4p7iTQ4Vd7XnzJNz78FfvNs447jHWtDW9S8PajoXha08Oz2kt8L60NhFakeZCqsDISByihAwbP416Nb2lraKq21tDAqrtAijCgD047UyGwsra4kuILO3iml/1kkcSqz/UgZNAHlFpJcWd14wg1HXtB095L+d7qHU7LzZJYWH7sgmRdyFMAAA4/GrMNpDp8XhNoPEw0/V4tJaO3uNUsgILmDcDsYM3yOBt4BzivTbjT7K7mjmubK2nli/1byxKzJ9CRxT7uztb+Hyry1guI87tk0Ydc+uDQBzPw+1Eajot4RZWVuYb+aJpbAn7PcsMFpY89iT+YNRaHZ20/xP8AF08sEbyrDZRBnUEhTGcgZ7HA/KuvjjSGJYokVI0GFRBgKPQDtQI0WRpFRQ7Y3MBycdMmgDxOFJ4fDng9xcWtrplrrF+hmvIfNt4H8xxCXXcOByAScA4rsfCq/aPH2qXp1yy1KcWMcVz/AGbZmODduJQs+9g0gG4Y64NdwbeBrdrcwRGFs7oyg2nPXI6UlraW1jAILS2ht4gc7IUCLn6CgDk/E9rBdfEHwWlxEkqK95IFdcjcIwQcex/lWBrN1aaTrHxFll09Lq2+xWLSWoyiyFgyktt5A6Fj6CvTzGjSJIyKXTO1iMlc9cHtSGGIs7GJCzjDnaMsPQ+tAHldzfSSeL/Bazazod5It6fLh0qDCwoYmGDJuPBPAHGcdOKfqVtPDrl74BjRxa6zqEd+jAHCWjZe5XPb54yMf9NK9Lg06wtYxHbWNtCgfeFihVBu/vYA6+9TmKMyLIY1MiggPjkA9RmgDzCSS7sPiV4kZ9Y0jSWljga3OqWvmB7YJjEbF1AUNuyvrXTfDi3jt/B8fkXn2u2kuJpIJFtjAgQuThEJOEznHPQiujurCzv1Rby0t7lUOVE0SuFPqMjipwAMBQAAMAAYAoA4vw1YWcvjHxvcS28Ukr3cMLM6Bjs+zqcc9smuT8NTWtnZ/Du71Z0TSo7a8iilnP7qO5L/ACbieAdoYAmvX1jjRnZUVWc5YgYLHpz61G9pbSWptntoWtyMGJowUI+nSgDzW90638Ua/wCMo9EeKS1k0uFHngIMbX6MzoQRwWAC5IrGbxAxaP4oGOTy47hbApg/6nyMMMf9fBr2W3t4LSBYLaGOGFfuxxoFUfQCm/ZbfyPI8iLyc58vYNuc5zj60AYGiWU3hv4eRRtNHHeQ2bzyzTnCCdgXdmPpuJz7VN4O0e30vQIZlnW8vL5Vuby+3bzcyMoJbP8Ad5+UdAMVvsFdGRwGVhggjIIpFVUQIihVUYAAwAKAFooooAKKKKACiiigAoPQ0UHoaALdh/yDrb/rkn8hViq9h/yDrb/rkn8hVigAooooA49/+Pu5/wCviT/0I1z+v6A+v6xpS3awSaNal5p4JOTNLt2oCuMbRkn610L/APH3c/8AXeT/ANCNQXl3b6fZT3l1KIreBDJI5HCqOprpS0Gczf8Ag21tb3S9S8N6fY2N7Z3QeTYvliaEgh0JA9Dx71LYeHb228Ma7psjxedfz3kkJDEqBKTtzxx15rfS+tpL02ayg3AhWcx9wjEgE/Ug/lWRr3inS9BuY4NQ1BLZ5E3qrIxyM4zwDVRgmKT5Vcz7vQtVFlptpJpulaxYQ2UcEtpdkIY5VABdHKnIOMY46ZFVB4O1SDwNFp9tcQR6rb3ZvLPMjNFbkscRqx5KhWYc+tXbPx1ol/N5Vrq8UsgRpDhG+6oyTnHYVr22pfa7aK5t5hJDKgkjcDhlIyDWnsL7MydaK6HPv4HmTWdAe3ljGm2dvFFexknMzQktEQP94knNPvfDNzbeIdS1C10fStXg1ArIVvWCPBKFCnDFWyhwDjrmuh+0y/3/ANKX7TKf4/0o+rMXt4mJb+Gr+NvDLyvY+Zp9xNPdC3iESfOjKAigc4JAycZxmuiLXx1RVEdv/Z3kks5Y+b5uRgAdNuM89c1QtdYjvXuFtroSNbymGXA+44wSP1FWBcS/3/0prDuw/bx7GjijFY15qqWFq9zdXAigTG526DJAH6kVP9om/v0ewkH1iPY0sUYrN+0Tf36PtE39+n7CQfWImlijFZv2mX++aPtMv980/YSD6xE0sUYrN+0S/wB8/lTIr4zxiSGdZEJIDIcjg4PP1o9hIPrEexq4oxWb9ol/v0ya/wDs0LTT3CRRLyzuQAPxNHsJB7ePY1cUYrN+0y/3zR9ol/v0ewkH1iPY0sUYrGbVUW/Sxa4AunjMqx45KAgE/mR+dT/aZc43mj2LD28expYoxWb9pl/vmq39swefcwG8jElqivOGOPLUgkEk8dATQ6LQe3ibeKMVi/2tH9ritftK+fNGZY0/voMZI9uR+dWftEv980KjJh7ePY0cUYrn9T8RWWjoj6hfx2/mEhA3LNjrhRyfwp8GuWtzpp1GG/heyCljOHGwAdcntip9j5j9uuxu4oxXPab4jsdY8z+z9QiuDHjeqnkZ6HB5wex71GvirTG1Q6YuqQG9DbPK3fxf3c9N3tnNHsfMPbrsdLijFZj3bxxtI0mEUFmPoB1qO11EXtrFdW8/mQTKHjcDhgeQafsGL28exr4oxWb9pl/vmj7TL/fP5U/YSD6xE0sUYrN+0y/36PtEv9+j2Eg+sRNKis37TL/fqzayu5bc2amVGSV2NVot2LNFHaisTYKKKKACiiigAooooAKKKKACiiigApr/AHT9D/KnU1/un6H+VCEdRpf/ACCbL/rgn/oIoo0v/kE2X/XBP/QRRXKxFGH79x/13k/nUtRQ/fuP+u8n86loAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACg9DRQehoAt2H/ACDrb/rkn8hViq9h/wAg62/65J/IVYoAKKKKAOPk/wCPy5/6+JP/AEI1w3xHuo0s7ayk8Qrpy3kscTwMI/mjMg3SEsMgAA+1dy//AB+XX/XxJ/6Eazr7Sk1DUtMupGGyxleXyymd7MhQc9sZJrp3ihnJeDr63vvF+szr4nj1GQ7II4gsQM0aIDvG0cKGdhxwa6nWADBNx/ywb+RqTRtJXSbR4t4lkkuJZ3k2BSTI5bH0GQPwpNSj84PHkgOhQkdsgitaK1M63wHHaEB/wqu34/5hT/8AoBrK0uXWdI8KeHdT/tXzIHNrC1kYFEYichRg/e3Dg5zg+lddZaNHZ+GI9EEzOiWxtvNxgkEEZx+NQP4djk8OWGj/AGlwtmYCJNoy3lEEcds4rdwdlbsc99zn7vxDPea1qcA1a606GxmNvCtvYGfzHABLO208ZONoxwK6bw3qNzq2g2t5eW7W9y4KyoUK8gkZAPIBxkfWqs+gXkWo3t1o+rtp4vjuuI2txKN+Mb1yRtbA56g4rV06yXTrCC0WWaZYlx5szl3c9yT604KV/eCVraHHaPYatd3/AImay1Y2KJqcvlqsKPvk2r9/d/D0GBjvzUz+JJr/AEPRbybVodIju42M4jj8yd3XjESkN8uQSTg9qv8A/CMXsFzqUmn67NbJqM7TTJ9nV9hIA+Q54OO/PbjinN4U+yXOnXOjXhsZbK1NookhEyvGTnkEj5s85FTaS0QaHL3+s3F94O8UW11cy3MdjLB5dxNb+TIyMyt8yYHT1wM10Gn+Iri/ttX11maKwsUkVLAptlJUbt0meVJHQDjHNSHwb5tnrEE+qXE0mqtFJNM8a5Vkx0AwMHGMdhWhNoEE2tT6grlVu7c217b7crcLjAJPZgMjPpUqM73G3HY5GDxPqa2VpqY1G7vLmVo2m09dNZYtjEZVH25yoOdxJBxW3aTazqPijVkj1MQ2Gm3MapAIFJmzGGKsx5A9xzz7VNB4Zv4oLawbxBdNpls6lIkiCSsq/dRpAclRgdgSK1LPS0tL7VLkSM39oTLIy4xswgTAP4ZpxjJ7idrHIaXr2oPfWceo629pqck22402+shFCy5IKxOByemDuOa6/XJ7+30O9m0yHzb5IyYUxnLfTucZOO9ZX/CKXk0NtZXuuS3Wm20yypE8C+a205VWkzkgHHOAT61ualYjUtPntGmmg81cCWBiroeoIP1q4qVncTepy+garJf6k9pD4jmmdoGMlve2ghuIX7Mg2gEDuDn61Q0Ce50z4dWk39tQWiyXDAzXEYPlJ5jAiNQPnYkcA+p9K6S10C7Or2eparqn22WyR1twlssIBYYZmwTk4+g9qoReDZoLG3t4tXfNldm5sWa3U+VndlWH8YO488EVFpFXRT0vXbo6vfWMeo3V9bDTXuoZrq08l1dTjA+VQy85zisvVU1rUPhydVv9aLi6S2l+zR2yKiZde/U9QT7j0rq4/Dc7arNqd7qslxczWb2bDyQqKrEEbQDxjnuc5qS48Nxz+D4/DxuXVY4I4lnCjIKEENjp1A4o5ZNahddDH1XW7iPxA+itq1zaxWlskk1zBZebLPI2cDAQqgAGenOaZF4i1a40yGzV9l9PqX2GK9ktigaLaX83y2A52gjHTNbEvh+++2RalbawYdTMAguZfswaO4UHIJTPBBPBBofwuZtJW3m1S8kvkuftaX7cuk3YqvQLjI29MUlGYXRm2tpfWfxFtI7u/a9j/suYxSyRqsg/eJkNtAB7YOB1rY1e+ubDV9FbzVFjcztazoVH3mUmNs9vmGPxqOx8P3UXiBdavtUku7kW7W/liERxqpIPABODxz659qua7o6a5o81g8rwGQqyTIMtGysGDD3yK0jFqLE3qjkoPF+otYa7LIVMrKsulLsAykkjRIPf5gD3607Xri/n07xfp0118tnp8DB1jXLExsXB46MR+HatuTwhaPeaJOssiLpKBFjA4mC427voRn61Nc+G4bqbXHkuJANWt0gkAA/dhVK5Hr1qOWTWo7pPQwZrTUW8R+HLeHU2SY6ZMZLlolLhMocKMbc9Bkjp71teHLy+e71fTr+6N2+n3KxpcFAjOjIGG4DjIzjNS2OhS295p95dX5ubiztpLYN5IQOrFSDgdCAoHvVqw0pbHUtUvBMzm/lSRkK4CFUC4B79KqEWncTd1Yh1M6TpFydfvjsnSL7Oj53EgnIVF/vE+nJrkrrT7iLSYpL23W2TWdfglks+MRxkjCt2ydgJ9zXRax4budT1y21SHV2tntoykMZtUlVCerDd/EeBn2qWTQLi+0eex1XVpbuRnWSG4SFYmhZSCpAHBIIzz9KUott6AnZFPXma18Z6NcQKBPLaXkblRyyqgZR+DD9ayJbeFPgskikCRLNLsSfxCbcH3Z/vbs89ea6aw0S4j1Uanqmofb7qOIww/uBGkan7xwCcscDJ9Kz18HOLVdLbVpW0NJvNWyMK7sBtwjMnUpntjOOM0uR3uO6N+7LPpM7N95rdyfrtNcfot3f3dh4c0OxvPsSf2Ql3PcLGrvt4VVXdwOckkg1208Qnt5YSxAkRkzjpkYrBHhdra30s6fqLW97p9sLVZ2hDrLHxlXTPTIB4PFXOLumiE9Cmur6pZJrmm3GoWn2jT0ilh1C6UInlyZ5kA4LLgjjGeKqadrc48S6bZ2+t3WpWl8squ9xZCJUZV3Bo22qD9Oa0pPByXOnX8d1qEsuoXssc0l4YlGGjOUATptHp3yeanXw/eT6zp+q6jrDXE9kX2RxwCOMqylTxknPTnPbFTaRV0Zg1/U28OJZGVRr5v/7NMmwYD7smTb0x5fzelQXfiC4vdZ1OAavdadFYzfZ4RBYGfzXABLOdpGMnG0Y4roB4atR4sOv+a/mGLb5GPlEmNvmfXaNtRzaBeQ6heXWkaw1gL077iM26yqZMY3rkja2Bz1BocZheJiT+IdXuNF0e7njvLC3l81dQntrQySRspwuFYEqrcnOD2FdT4Kv21DTppDqsGpospWO4iTYxTsJFwMOO/A7VTbQLmG3sBp+sXUNxZqy+bN++Ewbr5ikjJz0Ixj6Vp+GNH/sk30stybm7vZRNPL5YQE4wAFHAAAFTKMralwtc6Ciiiuc6gooooAKKKKACiiigAooooAKKKKACmv8AdP0P8qdTX+6fof5UIR1Gl/8AIJsv+uCf+giijS/+QTZf9cE/9BFFcrEUYfv3H/XeT+dS1FD9+4/67yfzrK8W603h/wAL32oRLvuVTy7ZByXmc7Yx/wB9EUAbRorgvBNrL4T1248J3Vy86z2seoW0sjFiz4CXC5P+2A2P9o1K/jPXZ4dautN8PW01ro9zNDO814YzKI+T5Y2nnHqQM8c0AdxRXH3XjWWW90mx0m1szNqNgL+N9RuvIj2HGEXAJZ+enYVcg8S3q3+hWmpaM1i+ptNC+6YN5UqDcoBA+ZXAYg8dOlAHSUVxNv8AESCXTPE941kUGjMfJXfn7UmSqMOONzqV71Zm8VancarPpWk6dYyXVlFE9895e+TGkjruESYUljjqcADigDraK4mbx9NJpOkXGnaR9ovNRvZLBrSS4C+ROgbcCwBBAK9R2OfauisrrWpLi1jv9Mt4Y3t2eeSK53iOUNgIBgEgrzn8KANSiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACg9DRQehoAt2H/IOtv+uSfyFWKr2H/IOtv+uSfyFWKACiiigDj3/4+7r/AK7yf+hGquoXF7bWwew077fMXAMXnrFgeuW49Pzq2/8Ax+XX/XeT/wBCNOjHzr9a6PslwMrw5rI8QaMmofZWti0kkbRM4YqUcqeR15Fam0H5tuR9K5LwE7w+CHlRN7JdXjKvqRK5A/SucGnwt8ND4w+3XH9u/Zvt3277Q3+sBz5e3O3Z/BtxWN2bXVtT1AIGOAufwoC842c+hFcX4g8jWNZ0yxksLzUrk2X2mTT1uRBbxqSB5kjZBJBJAHPfiqXhzT7rWfCvifQZJJbdrbUJbe0BnMrWxAVkUSdSFb9OKOd9xadj0EICPuikEYPRBmuIstVk8Xal4ei+ZBaRG/1FFJG2dSY1jP8AwMOcH+5WVPHeeIvEuv8A2nQrrVY7K6+y26x6kLZbZQoO5VyDubO7dz7dKOZ9x3XY9MCrnhR+VKUAHKiuIurDxBc/D7T9PvLmOPVvNRJYnuthu0ViTF5q/wATKBkj0NSeFksbHxFdWUVnqWkXLWwlbTbiXzYWUNjzY2y3OTg8jqOKOZ9w07HY7V/uis46tH/wky6GLZjI1mbvzQeMbwm3HrzmtOuJ1bSbPWPidb218sjwDRmYxJKyCT98OG2kEjnp9KfNLuN27Ha7AD90Z+lKUAGdtcHYXcWjaJ4wsZ9QvI9N0u4KW86P5k0KNGp2IzZyVLYGemapWtnPoviXw1Lb6RPpaXtw8E/magZ2uF8tm/eLyN2QDkHg0uZ9xadjttM1aPU7/VbRbYo2n3AgZic7yUDZHp1rT2DOAorye/1G8tvEPiOzbz7PRJ9ViTUdUiI3RI0SgIO65PBfsD710njZprLT9C0fTYZTa3d2lu0UNx5TSRqhYRiQ9N2AM5yRn1o5n3ErdjsyoHVMfhWb4g1WPQNAu9WktmmS2UMY1O0tlgOD+Nc54a07VtK124ki0eTS9IksyTavfi4HnqchkGSVyCQfoK5m+0e1vvhFceJLma4n1a5iE8tx9ocZJlAMe3O3aOm3HajmfcNOx60FBUHaBmlCr/dFA+4v0qnq5K6PdMst3EwTIezjDzDkfdUg5P4U7y7lWXYubV/uj8qNq/3QPwrhtIuLhtWtA2qeL5QZB8l3p6JE3+8wQYH413VLml3BJPoZes63a6KlsrwTXF1dSeVbWtugaSZgMnGSAABySSAKZo+u2+sT3VsbS4sr60Kie1uVAZQ33WBBIIPPIParGr3enaVanVNQCAWwIjfZuky3G1O+W4GB1rL0LTb9pNT13U4jDf6jGFW2HP2eFAdiE925Jb3OO1HNLuS9yI+N7EA3R0++OkrN5J1Pyl8jdu25+9u27uN2MVo6vrsGl3dtZR2VzfX1wrPHbWqru2DGWJYhVXJAyT1rzZU1XUfgwXllGnaTaacVSONgZbtwcfOcYRN3G0cnvjpXR3cmrRfEG3g02GJrufQY0824b5IQJcs5HViOPlHUkZwKOZ9xJ+R1ej6va63ayzQRSwyQymGe3nTbJDIOqsPoQeDg5q/tX0H5Vx3gq0nstf8AFsM11Nd7byEG4lIy7+SC3TgdRx2GBXZVtBu25nJJvYTavoPyo2r6D8qWitLsmyE2r6D8qNq+g/KloouwshNq+g/KjavoPypaKV2FkJtX0H5UbV9B+VLRRdhZdhNq+g/KjavoPypaKd2FkJtX0H5UuAOgxRRSux2QUUUUWAKKKKVgCiiigAooooAKKKKACiiigApr/dP0P8qdTX+6fof5UCOo0v8A5BNl/wBcE/8AQRRRpf8AyCbL/rgn/oIorlYijD9+4/67yfzrnvE/h2bxJq2jW91HFJolrI9zdxu3MsgXEaY9ASSfoK6GH79x/wBd5P51la/qraWvnNOIYEiMkjkDCgdTVQg5uyE5WVzD1PwLa2V9pWreFtOtbTULG6V5AGKCaAgrIhP0OR7irmm+H7610DxNZSiPztSu72aDD5G2UYTJ7e9RjxHLJdC2W7BuDEJtm0Z2E4B6VFqHi1NHhSTUdShtUkO1GlwAx9BXR9Vla90Z+2RUu/Duqpo+iWF1oOl6/YW2nx289nM6pJFOqgeZHIw5UgEY4Peo4/BmtQeAvsNvLDHq9vqH2/TojM0kdrh8rDvPLAKWGfep7DxxY6ncfZ7LXbOeYjIjR13H6CtT+173/nsR/wABH+FCwk3s0HtkYMnw/vE1HwzHbyQiwtraGHVVZuZjC/mpj1zIWz7GpNX8HSJ4r1LVh4Z0zxDBqXluUu3RJLaRV2nBZSChAB45Bra/ta94Pnf+Oj/Cj+170/8ALb/x0f4U/qc+4vbxM+z8J39tF4Xzb6bbvY6hLd3cVlGIokDxuoCDHzEZUE9TjNdY0l8NWjiFtCdPMBZp/M+cS5GF246Yyc5rD/te95/fc/7opf7Wvcf67/x0UfU59w9tE6aiuY/ta9/57f8AjopkmtXUSF5LlUUdWYACl9Un3Qe2idVRXMf2te/89v8Ax0Uf2te/89v/AB0U/qdQPbxOnorlv7Zug6oblQ7AlVIGSB1xTv7Wvf8Ant/46KX1OfdB7eJ09Fcx/a17/wA9v/HRUMHiCa6Eht7xJRHIYn2gHa46qfcUfVJ90P20TraK5j+1r3/nt/46KT+170D/AF3H+6KPqk+4vbROoorkIPEjXVvDPBfxSRTkiJxjD4z09eh/KnQeIJrkyiC8SQwyGKQKAdrjqp9+aPqk+6H7aJ1tFcv/AGte/wDPb/x0VmXfjqwsbl7a712ygnT70ckiqw4zyDQ8JNbtAqyex3dFctHrV1LGkkdyHRwGVlAIIPQihNZupAxS5VwpKkqAcEdR9aPqk+6F7aJ1NFcx/a17/wA9v/HRR/a17/z2/wDHRT+p1A9tE6eiuY/ta9/57f8AjoqKbX5rd4EmvEjed/LiVgAXbBOB74BpfVJh7aJ1lFcx/a17/wA9v/HRTJNauoonkkuQiIpZmKjAA6mn9TqD9tE6qiuUg124ureO4gulkhkUOjqAQwPQin/2ve/89v8Ax0Uvqk+6F7aJ1FFclJ4gmiuIbeS7VZ5wxijIGX28nH0FFr4gmvbZbi1vEmhYkK6AEHBwf1FH1SfcftonW0VkaRe3F1NIs0m4KuRwB3rXrCpTcJcrLjJSV0FFFFQUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFB6Gig9DQBbsP+Qdbf8AXJP5CrFV7D/kHW3/AFyT+QqxQAUUUUAce/8Ax+XX/XeT/wBCNUtSsLXUrdbe7mnjQMHBguHhbI/2lIOParsn/H5df9d5P/QjXMeL9ftdA06ad7i2W7EJaGGWQAu2cDjqRn0rqhFSVmDk4q5b0bw3o2gSh9OkuUwG+R72SRPmOSdrMRnPOcVGfCPh43Bk8uTyjN55tPtL/ZzJnO7ys7evPTGazNP8RW+o+ILjTra6s54obaOXfFKGZnYtuAwegAH/AH1U2sS67CRJpMWmvCsZaT7W8gbI9NoxjFafVoNXTI+sy2sauraDpOtXEN1dNLFdQqUSe2uGhk2k5KllIJHtUuj6RpGgxXEemosEc8nmyL5hbL4AzyfQCuP0jXPEN5oqazc22lJYvavcBYnk83hSVByMdRzzWlo3iOx1SzsPMvbRdQubdJmtUlG9SygkBc5ojh4PqH1mS6HQ2GmaVpl3fXNnHHFNfS+dcMGzvbHX279PU1U1Dw5o+o373zPcW91IgjlltLt4TKo6BtpG7FV7zV9N02SOK+1C1tpJPuLNKqlvoCanM8PnLCJY/NdS6puG4qO4HpyOar6rDuL60+xNcaFolzosWkPbxiygIMMauVMZHRlYHIYeuc0aboml6VcS3UEs0t5KnltcXVw0zheoUFjwM84FQrcQvK8KyoZYwC6Bslc9MjtmoL3VtN0140vtQtbZpPuCaZULfTJoeEguofWn2A6b4g/6HFf/AAWxf41a1PQNH1a8jvbppVvI4vJjuILh4nRc54KkY5z/ACqG5vrOztjc3VzDDbjB82SQKp9OTxWZrHiCGz8LXetadJb3iQqChV9yMdwGMj60nhYdx/WpdjctdC0S00WXSI4I2sp93nJI5cylupZjySfXNU7Twf4ftLq1u1NzLcWrh4JJ7ySQxcEbV3HhcHp9KLzU7DTzF9uvra1804QTShNx9smq2g6o2r2U9w8aL5d3NANhyCEcqD+OKFhoN2uL6zLextx6XpSDUh5SMupOXukdtyyErt6HoMACoG0LRpdBj0SaPzbGIARpJMxaPH3Sr53Ajsc8Vz02s6rd6zeWGjWdo6WOwTzXUjKGdhuCIFB7YyT60q+Irq/0eyuNL00y3V1K0LJMSEtmXIcyMB0BBAx14pfV4dx/WZdjotM0bTNJuHuYJZ5bl0EZnurlpn25ztBY8DPOBWdceCfDV156yxTeTO5ka2S7kWEOTksEBwDnmqujavdXd/faXqNvDFfWQjZmgYtHIjglWGeQeDkGr1xcSx6nZW6NbeVMJDIHkxISBxsXv7+lNYaDV7ieJl2OgE0QAG9QB70vnxf89F/OubOuaR9pitv7Ts/Om/1aeeuX7cDNW5JI4YnlldY40BZnc4CgdSfSn9Vh3H9bkuhsefF08xfzp3nRf89F/OvPYvGcV3pWk31skGy9vltZlaUHyVO7kkdDhQefWuhstTsNTWRrC9t7pUO1jDKH2n3xUrDQezH9an2Lms6NpWvrbrqBlP2aTzYjFcvEVfGM5Ujn0+tJpmjadpFw09tdXjOy7SLi+lmGM56OxAPHWqMWr6bPfNYxahayXa8GBZlLj8M1bkdIY3kldY40BZmY4CgdyapYSD6kvFPsV/8AhEvDxumn8uXyzN9o+y/aX+zmTOd3l525zz0xmr2raPpesvbzXTOlxbkmK4t52ikTPUBlIOD6dKzk1nSnuZbZNSs2niUvJGJl3Io6kjPAqSyv7PUYDPZXcFzEpwXikDAH3IoWFh/MH1qXY0NL0/TtGsvstiAkZdpGLOWZ3PJZmJyxPqau+dH/AH1/Oues9Z0zUJnhstRtbiVBl0hmVmH4A0HWNMWbyTqVoJcM2zzl3YX7xxntg5+hqlQgluS8RJ9DofOj/vr+dJ50f/PRfzrAi1jTJ737HFqNrJdY3eSkyl8fQGmwX5D3f2yazjjiuBDGUl5GQMB89Gyen0p+xj3F7eXY6Lzo/wC+v50edH/fX86wLPVdO1F5Usb+2uXiOHWGVWK/UA0xtb0pLmW2fVLNZ4hmSMzruQe4zxT9hHuHt5djovOj/vr+dHnR/wB9fzrLGSPrVBNc0qW++wx6nZvd5K+Qs6l8+mM9faj2Ee4vbvsdH50f99fzo86P++v51z13rOmWBb7ZqNrb7WCnzZlXBxnHJ645qae+tLW0N3cXUMVtgHzXkATB6c9KPYR7j9vLsbfmx/31/Ol82P8Avr+dclp+upqWuXllbtBLaw28M0c8Tbt+/dnkccbafq97qUE9raaVZJPPcFi005Kwwqo5LEDJJ6ADr+FL2UbXuP28r2sdT50X/PRfzo86P++tcL/wlb2mk6rPqFkq3mmTLBJDA+5ZXbbs2k887h16VPb6vq1pqVlaa5Z2kQviyQyWsrMI5AN3luCByQDyOOKnkh3H7WXY7TzY/wC+v50nnR/31rn9TnltbLzYDbCTzEXNy+xMFgDz64PHvikvNW0zT932zULS32sEbzZlXBxkA5PpzVujFdSfrEux0PnRf31o86P++tZKMkkaujK6MMqynII9Qa5q68X24i8Qi2MDz6TGWVDKD5pCbjwOcA4B980nSit2NV5Pod350f8AfX86POi/56L+dc3Y61p166W0d/aPeeWHeCOUF14yeM54p9zq+mWdyltdahawXEn3IpJlVm9OCaapRfUXt5djo1YMMg5FLUFp/qFFT1hNWdjeLurhRRRUlBRRRQAU1/un6H+VOpr/AHT9D/KkI6jS/wDkE2X/AFwT/wBBFFGl/wDIJsv+uCf+giiuViKMP37j/rvJ/OvNvi9Oy21paDWoLJbzbA8EiIfMVpAGbJOQAM5xXpMP37j/AK7yfzrlfF+lR6jq2kTylPLs3eVkZAd5KlQM+g3Z/AVrRTcrIiexwfhec3PivVJm8RW18Y1S2RI441MqKu7cNp4ALkcdaueL5pLfWPDMsNm95It5Ltt0Kgv+6PduPfn0rZ0bR49JglGIpJ5biWdpFiC/fYkD8BgfhRqGlC+1LSrzzdhsJnl2bc79yFce3XNehyPk5Tmv71zlbm5uPEniPT9L/skaTcafMl9JJcyJ5jICeIwv3gehOcDvWrBca1r95qb2Gox6fb2dw1rCpt1lMrrjczk9Bk4wMVoa1oY1Saxuobg2t9YzCSCcJuwP4kI7qw96qS6BqNrfX02j6slnFfv5s0ctt5vlyEYLxnIwTjocjIpcsolXTM2z8Saj4gXR7S0kisbm6immu5hHv2CJ/LIRTxy3c9BUVx4l1XQo/Eg1CaC8fTVtxasIfLDmXIBcD3xnHpx1qTUtPsfDiaJHb3lzYPapJHHftD50ZDYLLMP9o8g+oNQaJo418+JWu7qe6s9QaFI7wx+UXZF+9GuOFU4x9O9TeS0vqCSH6d4mmTWNNtjrS6ul3L5M6LYtCYSQSHU4+7ngg88g1peHLrW9Vuri9ur6JbKC7uLZbZIBucKxAYt2I4GB6e9W7TS9ZN7ayalrpngtTlYoIPJMzYwDKdx3fQYGat6LpQ0i0mtxIZfMuZbjdtxjexbH4Zq4qV9RSt0MvxVqt5pclgI5ZbSwlL/ar6K289oiANo29gST82D0rD1m5u9V8EecmtWl0q6jAqXNtECZEMi7d6n7rAnOB1wPWuu1TT7+5uLa603UjZ3EG4FXQyRSq3UMuRyMcHPHNZLeEGl0jUoJNQUX9/cJdPcRwBUSRCCu1M9Pl55yfWiak2wTQmt6hdWF7Fa3XiW2sIxADuS3EtxcSdz5eCFXp0HU1nReJtXuvDmnyW80P26XVjpzTPBtVwNw3lDyp6HHtitkaFq0GsS6paarbR3V1DHFdmSz3glBjdH8w29ehyKjtfB4tbW2tzqEkoh1T+0vMkQbnPOVOOOpJz+lTadyvdsUDp+rQ+PNHW61vz3WxmZn+yIm5Q6ZXA6ZyOe2Pesv/hN7i5srjVItZjhlVnaDS/sTOrqpICtIBnc2OoOBkV2WoaRcXOu6fqlpeLBJbK0UiPFvEkbFSR1GD8vWs5PDOp2ltNpmm64bXSZHZlQQZmhDHLLG+QAMk4JBIzQ4yT0FdPcRNT1HXtXaz026/s6CC0huJXaASSM8oJVMNwAAOe9O8DLOthqou2Q3I1W580xghS2RyM9qt3eh3aamNR0jUBbXLwrBOLiHzllVfuk8g7hk855zU+gaM2iWc8DXb3Tz3L3DyuoUlnwTkD3qop8yuJtW0MjUdev9PfxBalka6i8ltOyoAIm+RQfXD5o0zX77Um0C3DIs8sc0mofIOBF8hAHbMhH5Vo6n4bi1PxBpmqtO0Zsj88YXiYA7kBPba3NGk+GodK1rVdSWdpDet+7jK8QqSWYD6sc0mp8we7Y5KGXUNWl8DXn22O3lma4yI7ddqkK2SB05XjHbrVvToNZaXxTPpupRWccGpTyLGbcSeY4VSQxPRcYHHPWtVPCM1tpuhwWWorHdaS7tHM8O5XD5DArkdj61p2GifYYtVj88sNQuZJydmNm9QMdecYoUH1C6sT6NftqmhWGoOgje5t0lZV6KSAeK5VbLxDph1nUf7L0q4je7luTHcMWmliAAAUgFV+VeAa6vR7D+ydGstPEhl+zQrFvxjdtGM4rGl8NamIryxtNcaHTbt3Z0e33zRB/vKkmeAecZBxmqkrxQk0mben3kWp6VbXlsCkVxCskeR90MMjj2rg9O1a98OeD7m4a8SaS51aW2haSDCwsZW3SELyw4LY/CvQbS1hsbOC0t12QQIsca+igYFc6PCMhsryyfUyIWu/ttk8cIElrNuL5znDjJ6YHFE09Ggi1rcqaP4kkfX7fThqv9rQXMUhMoszC0DqM88AFW5x3GKpQ654j/AOEOtvFEmo2zKCpeyFqNsib9hy+chj144rp7LTdVOoi81XWftASIxx29vCYYuerMNx3H07Cqw8LKPBKeHPthwqhfP2cnD7/u5/CpSk9xtxIZJ9b1LxVqul2WpR2VpaRwusn2ZZH3OpOOTjHGarWWvX13Z+HZbkQefPqctpcMsYIbYJBuXP3Sdo6V0Fppa2uu6jqfnljerCvl7cbNgI6985rPtPDC2sWmxi73fYtQlvc7Pv79/wAvXjG/r7UWl3C6MQ+I9Q/tOaG71i302+W6KRafeWhSGSLdgYl6kkc5B68Yrr9Z50LUjj/l0l/9ANYV74X1G9tbnTJdbEmlXEhYrNb+ZOik5KLITjHocZArorq2W40+e0D7RLC0W7rtyuM+/WqhfW4m1oziNPudb0LwXpGry38U1rHDbrLYiAACFtqgq+c7xkE9q0Na1aeHVbq3PiO10/ywPs1tBbfaZXOM5kGCRk9AO3epIPCd59gsdKvNYW40m0MbeSLYLJLswVVnyflBA6DPA5qwPD+oW2oalLYatHBb6jMZpg1tvlRiADsfOAOOMg4qEpJWRV49TL03VJta1PwTqU6qs08F0XCcDIUA4/KqGm6ne6Z4M0YwSSW1nJcXAu7yK289oQJHK/L7njODiuk0vwsmm/2GFuy40lJkXKY8wSevPBH60238NXenaXYW+nau0NxZySMHeLdHKHYkq6ZGcZ4OaOWW4cyOg+HeoSahBdyNqlpqcYbEVxbrsYrxw6/wsOf0ruK4nwNozaZqGqXdxcLPe3xWSZ44hGg28AKuT27k5NdtXFXvzam1O1tAooorE0CiiigAooooAKKKKACiiigAooooAKKKKACg9DRQehoAt2H/ACDrb/rkn8hViq9h/wAg62/65J/IVYoAKKKKAOPf/j8uv+u8n/oRrhfH6Xdxf6JY2thZXPn3CnM8hU/uyZCv3ThTt5P6c13T/wDH5df9fEn/AKEazr+KN7mGVo1MkYOxyoyueuD2zXZTjzWRM3aNzivCK381/qV/Lp+nww3F048yGUsymPEe1RsHy5UnOR9K6i7P+g3BP/PJs/kakiijhjEcMaxoCSFQYHJyeB70/AIIIBBGCDXbCHLGxxyld3OU0Lj4V2/T/kFP/wCgGsdrG0tvhx4amitoklWexlEioA25nXcc9ecmvQI4YooBBHEiwhSojVQFA9MelN+zQeSkBgjMSY2IUG1cdMDtipdPSxXOcHGZ18SeJhc3ejQSGf5l1KEsxt9g27TuA2Yzx65zRDANL8J+HtbiuftMemSndN5TJutJGKsNp5woKkZ7Lmu4u9NsL9ke8sra4ZPumaJXI+mRVDxBpd9q9iun21zBb2k4Md2WQl/L44TsCRkc+tS6bSGp3ZX8JRPNp9xq84xPqs7XJ45WP7sa/wDfAB/GsvVogviq+msNR01L1rWNbm01WI+W8Yzgo/UDrnGRnrXZxxRwxJFGoVEUKoHQAcAVXutNsb4obyytrnZ9zzolfb9MjircLxSJUtThdMvLG6vvClzdWkdjpptZ0toZW3RJOGAGC3ByoJUn14qt4hNq8PjR9NKfYja23nGL/Vm43/NjHG7bjOPxr0aaztrm2+zT20MsGAPKkQMuB04PFSRaTb/Yvs0VpbrbHgwiMBD/AMB6VDp23ZSld6I5S3axj8caydXa3V2toPsxudoUwbTvC7uMbs5x7VL8Pvs3/CMyfY9v2b7dc+Tt6bPMO39MV091odvfhBeWltcBDlBNGH2n2z0qeLTPIQpFHHGpJYhAAMnqaUeVSTuNqTWxxN2I7TxNqklhr8OmSNHHLfRXkIZSAuFljJYc4GD1GetQ+F7+20XwzYxX0k8UWqXU5guJDt2hmYoXbjaWHI9zXa3Wg2166Pd2VrcNH9xpY1cr9MjipZ9KW6haC4iimhYYaORQyn6g0WSd0x2drWOL8NRw2Hi7VtP06ZrmxMMc80rv5rRzk42GTq3ygHBJxV7VTjx14ZH+zd/+gLXS22jx2UXlWtvBBFnOyJAgz9BT203dKkrJEZEzscjJXPXB7U1y8trkuMr3seWpYWY+DV7cLbxecRLL5u0bt6zHBz1yMAV6Si+ZbqGAYMoyCM5yKm/siP7MbbyIDbnIMWwbTk5PHTrUv2KUYwVGKIuK6g1J9Dyeyisrzwb4UtCIJQdYRLiMYPO6Q4Yflwe1beswyweKtQTS41jupfD0pVYl2lnD4U4Hcdq7WPQ7eFmeO0tVdpPNLLGAS/8Ae6dfepv7O/fCbZF5oXaJMfNjOcZ9M1No23K97seUQxJL4V0VE1XRoY/MgNv9mtWNyJsjIGGJ3ZyGOO5zXeeKD/xSeskf8+U3T/cNacegWkV2buOytEuWzmZYlDnPX5sZqxJp7SxtG4jdGGGVuQR6EU48qTVxNSb2PPbrSdO+xeBoxZwbTNED8g+YGEsc+uSATnrVTxHE8N14zjsoyitaWbypCMZTLCQ4H+wDmvSzpYbyt0cJ8o5jyo+Q4xx6ccUo00rK0qpEJHADMBywHTJ71No23GubscRq76TJeeGP7Ea0a6F7GYPspXIt8HzM46Jt61R0yws08IeK70W8RumuNQ/fFBuAG4YB6gY7e9d/baFbWUjyWllaQSP99oowpb6kDmpV0pVieJYYRHISXQKMMT1yO+e9O0b3bD3trHAXdjZ2fhrwg9tbxROL2yw6oAx3D5uffPPrVPVoUuLDxHDIoaOTxFbqwPQg+UCK9MOmBkRGjhKoQUUqCFI6EemKadIjbfmC3+dxI3yD5mHRj78Dn2pOMX1Bc3Y5DU7VLfxvpa2EEUM0mm3aKI0C5wFKg47A1y9/c6D/AMKmjt2e1/tFFQNE2PPW5DDeSPvZzuyT2r1l9M3yLIyxGRQQrkDcoPXB7Vyc/grWb+M2V/eaZJbOwE92lsVu50DBgrH7o6AEj8qUrJ6MaTa1RvX0622mXM7o7pFA7sifeYBSSB715nfTr/wi2jyA6JaWrXVvJbWlsDJMo3qeZCeCBnccdeK9d+xSc/dx6ZqnH4csoTIYtPsYzL/rCsKjfznnjnmrnaStclKSexzGk2Vnc+N/Fck9tDK++3j+dQ2FMXI56Zrn9EaBLPwW+olTp6fa0QykeWs4YiPdnjoGxnvXqKacY5HkSOJXkILsBgtgYGT34qJtFhe1Nq9tbNbnrCUBTrnpjHWlaNtx+9fY4/QDp58e+Im07ySnk2/mmHG0yfPnpx6Z981t6nqllb3Vvpl7JJAL5HWOYNsXI6rvzw2DxWtb6NFaKFtre2gAULiJAvA6DgdOTS3OkR3kPk3UEE8Wc7JUDDP0NUpJK1yXFt3sebQ22mpp/ivTGuZZdBt3jmW7iHmSJMfmf5xy5VgpyckdKnniu7/xF4bguNfg1SaKf7VttIgirEqHMj4J5JKgdB14r0KDSUtoBBBDBFCM4jjUKvPsOKZaaHb2AYWdpa22/wC95MYTd9cCoUY9yve7HOeOD/xTfX/l7tuf+2yVDpVjaXPjrxTLPbwyyBreLMihsKYskc+v9K6+XTfPTZKkUiZB2uMjI5HWhdN2SSSIkSvIQXYAAtjgZPerlyt3uJKSVrHK+Asf8IZYgZIVpVXnsJWAH5CsG+a2jj+IMWYkuPKDqnAcqYBkgdcZr0iPTjCgjiSJEHIVBgfkKik0SCWdp5LW1eVk8tnaMFmT+6Tjke1D5WkrglJO9jitSsLOxt/BzWtvFC0eoW6KyKAdrI24Z9+/rWPZhzB4kS+vNEhY3c32xb+BmlKfwc7hlduNuB9Oa9RbTN/lh44WEbBkBAO0joR6VBc+H7S8nWe5sLOeZPuySxKzL9CRxUNRvoxrm7B4TRo/CWmI9w9wVgUec6FC4xwSDyDj1rZqKCMxxBWxkE9KlrGW50x2CiiikUFFFFABTX+6fof5U6mv9xvoaQjqNL/5BNl/1wT/ANBFFGmf8gmz/wCuCf8AoIorlYijD9+4/wCu8n86o6tJFArTzELHFGzu56KoGSavQ/fuP+u8n865rx7p9tqPh+8+0iQiG1mkTZKyfMEOM7SMj2PFNaFR3LVvLBd2sN1AyyQzIskbgcMpGQfyqQICM7K5rwXpNlYeGdNvYEm82bT4S+6d3B+QHhSSBz6CuJsDruv+HTrVtpusy61cM81vexX6JDEQ52oIy+NgxtIK5PNVzPubadUeuBB/dB+lJsAbGz8xXF6hDNqnxA02xvJrqG3l0Zpp7aCdowziRRglT2z2Pb0rMupJ9O8KePNOgurkw6a3+iNJMzPErRq20MTnAJOOaOaXcHbsekbQO3X2qte3H2OOJzaXE/mSpFiGPcV3HG5vRR3PauQ1DTv7D1Dw1qFpeXzXN1fxW1201y7i4V0bO5Sdo5GRgDFHjjTYrS50vVree8ju5tWtIn23UgTYWwRszt5x6UuZ9x6W2O22L2QflSlAOqj8qxPF9vqN3oZt9LuBFcPOm5PP8lp0By0av/CzAcGsnwx9ltvEVxaRJq2mzvaiRtMvnMqNhsebG5Zs9cEA9+lHM+4adjsNi/3RWdJq9uniaHQzbuZpbRrsSDG3aGC4x1zk1p1xGs6bBq3xS0+2uXnEP9jysywzNGXxKOCVIOO+PanzPuOSS6HbbFH8Az9KUxjAJjH5Vw+nXQ0e08YafNql1HYaU6tBdM3mywLJEGKgt94qemfUVn20NzpOveGprew1WxjvLn7PO97qHnG5Uxs3zpuOGyAc8Y6Ucz7i07HT6x4jfRb23iuNFna0nuYrVLxJI9u6Q4Hy53YB9u1a2oTXFpbCS005r6XcB5Ubohx65YgVzvxAH/Et0b/sN2f/AKHXXcl/xpcz7hZXZlaDrEWv6X9tS1e3xNJC0UhUlWRip5HHUGtPyxtztFcx8P8AH/CPT57ald/+jmrmr62mtBfX+stq+ftDywa7pt2ZYoI93ygxBvlVRwRtI680cz7grW2PSwi/3RS7V/uCkjIeJHVw4ZQQw6Nx1qrqshi0m7cTTwFYyRLbxeZInuqYO4+2KfM+47LsW9i91FGxfQVwuk38smsWqHxD4knDSAeVcaMIo39mbyxtHvkV3ffpRzPuCSfQoaxqlroekz6jdq5hhAyI13MckAADuckVBoWvx6tqa239jaraYG4veWhjQ4IGM5689PY1F4q01NY0Uae2ox2DTXEXlyyKGyyuGCgEjJO3pVXSZ9X0rx3baNf6odTtb22kuIpJIlSSFkZQQdoAKndxx1pXfciVux3vlRf88k/Kl8qP/nkn5U6ild9zn0GeVH/zzT8qPKj/AOeaflT6KLvuFhnlR/8APNPyo8qP/nmn5U+ii77hYZ5Uf/PNPyo8qP8A55p+VPoou+4WGeVH/wA80/Kjyo/+eaflT6KLvuFhnlR/880/Kl8uP/nmn5U6ii77hYaqIpJVFXPXAp1FFIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFB6Gig9DQBbsP+Qdbf8AXJP5CrFV7D/kHW3/AFyT+QqxQAUUUUAcg/8Ax+XX/XeT/wBCNYXiDVrXSBA1wZWeZvLhhijLyStycKo68VuP/wAfl1/13k/9CNcZ43ur22v9JWJ57aykZxdXttbedJDgfKAMHaGOQTg12U21ZomorxI5fFds+k6nPbwXaXdlEWe2lt2EiEg7SV/u8dR6VgaX4nvrX/hH0uH1G8OpbpJ2ls8EfuwQIwoHy5Oc88UafBPJqPiaRE1SaK40sLBPeod0xAcHaMDAyRhcA98c1ZfzLCLwTdzW1yYrWMpP5cLO0TNAFG5QMjnit+aT1OeyWht3nivTrK6nhKXU4tTi6lt7dpI4O/zsB6emcUuo+KdL05rZXead7qLzrdLaIymVePugdeufpXJRwnRpNWtb6516GaS6mnhisI98dyrnI2nYfm5wQSMVsaTpkljr/h9Pss8UVvpEsZErbzExdCELgYzjP5UKpPYXLEvzeMNNhmmhWK+nmgwZo4LVnaNSobc2Ogwf0PpVq58SaZBp9neCV5473H2WOCMvJNxnCqOfrnGO9VtEglj17xNLJC6rNdRlGZThwIVGQe4zmua0a3uNGsfC2p3Vlc/Z7a2uLe4VYWaSAu2VcoBnHGDxxmqc5oXLE6+z8R6bd2t3MZHtjZc3UVyhjeEYzllPbHQjg1HY+KNPvryC28u8tmuQTbNdW7RrPxn5CevHODg1y+r2N54nk12/sLabyGsobeFZkMRu2SXzGwp5xj5QSBnNaGo6iviW40W10+0vUeG+iu52mt3iFsqZypLADcemBnvR7SQ+VGhceNdJt2uPlvJUtZGjuZIbZnSAqcHcQOOn5c12FjKk1lFLEweN13Kw6EHkGuA020nXwv4pjNvIrz3l80aFCC4YEKQO+e1dj4XjePwrpUciMkiWkSsrDBBCjII9aipKTjqaUkuY1qKKK5zpCiiigAooooAKKKKAsFFFFABRRRQFgooooAKKKKACiiigAooooAKKKKACiiigAooophYKKKKAsFFFFAWCiiigAooooAKKKKACiiigAooooAKa/wBxvoadTX+430NIR0+mf8gqz/64J/6CKKNM/wCQVZ/9cE/9BFFczApQ/fuP+u8n86paqInYRTBWR0KsrdCDwauw/fuP+u8n865nxtqcOkWcl9Ky4gt3kCs2N5AJ2j3NVSipSsyXNx1RU0XQbHQGItL2+aAJsit57syRRLnOEU9OlVn8IaK9xMyXF5BbzuZJ7OC8aOCRj1JQHv3AIBqlpuuJf6pNZfuMxWsMrMkob53BJQfQAc+9O1LUtQi1GHT9LsEuJ3jMry3DFIYkBxyQDliew+tdaw0Gr3IeJls0b50+wbWYtWJ/0uK3a2Uh/lEZYMRj6gc1Xn0HSbmDV4pASurEG7Al+9hQvHpwBWToerzaol3Fc2otbyynMFxGr71JwCGVu4IINaMlzbwyJHLPFG7n5FdwC30BPNNYWDV7g8VLaxdvNPsL8WQuDkWU6XEAV8YdQQM+o5NZ+r+F9K1y5E15e6hw6SLHHesiI6/dZVHAI9afLcW9uU8+eKLecIJHC7j6DPWnPLHHne6rgFjuYDAHU/Sj6pDuL61J9B0+g6Xd6Qum3c1xcxJIJUlmuWaZHByGWTO4EdjSaZoOn6ZePfC6ury9aPyhcXtyZXVM52jPAGeuBzTY54ZYRNFNG8RGQ6sCuPXPSkguILqPzLeaOaPpujcMM/UU1hI9w+ty7DDZ+IskjxNZD0H9mrx/4/Ump+HdM1a/g1C4uLiO/gh8mO5trgxMgJySMe+euetNluLe3ZRNPFEXOFEjhdx9s9azdc1h9LFlDbRQy3d5OIYlmk2IvBYsx64AB6UnhYLqNYqT0sa1n4d0Wz0W50kIZba63G5M8pd52b7zOx5J6c+1Urbwbo1vd2d093f3M9lIHtmub5n8oAY2gHjH+A5ovNQXTNEl1G82H7PAZZBG3ysQM4Un17VYhuIp7ZLhWQIyBj84O3Izyaf1WHcPrT7DdW8L6Xrlz597eX/EiSpHHesiI69GVQcAjrn1q42k2zaWun/2jqGxX3iYXr+cT6F85I56VWjuLeaHz4p4niH/AC0VwV4689KVbiEzGFZ4mmA3GMOCwHrjrR9Uj3F9bl2E0fw3pugy77G9v9uXYxS3jPGWbkttJxnPOarS+C9DkMqCe8js5pDJLYR3jLbyMTk5T0J6gYHtVh7u3iI8y4hTc2wbpAMt6devtVeea8TVrSCKKFrWRHM0jS4dSPu7V7+/pR9Vh3D61Lax0gkhUAKyKoGAARxS+bH/AM9F/OsRbi3a4aBJ4mlUZaNZAWUe461najq0llrej2IjQpetMJHY4KBE3Aih4WCV7lfW5bWOs85Mf60fnR5sf/PRfzrEhmiuIhJDNHIh6MjBgfxFJDcwXDOsFxFLsO1/LcNtPocdKPqse4vrcuxf1TT9P1mxeyvgJIWIbh9rKwOQykcgg9CKg0TQdM0jVTf/AGm5ur11ERur25MrrHnO0E8AZ5965O18U3M+i6Jfm3iD6hf/AGR1BOEXc4yPf5B+ddK1xAs4gaeMTMMrGXAZh7DqaX1aD6g8TLsdkbu2H/LxF/32KPtlt/z8Rf8AfYrkDgKSSAAMknoKZDNDcx+ZBNHMmcbo2DD8xT+px7mXt32Oy+2W3/PxF/32KPtlt/z8Rf8AfYrjI7i3mkkjhnikeP76o4JX6gdKDc26zrA08InYZERcbiPp1pfVIdxqu+x2f2y2/wCfiL/vsUfbLb/n4i/77Feb6XriXkmordNb2/2bUJLOLc+3zNoBHU9ee1a0zSRwStEitKEJRXO0EgcAnsKFhIvZg67XQ7L7Zbf8/EX/AH2KkSRJF3RurjplTmuAtr1hpdvdai1vayug8wCYFAx7Bu9ddoJVtOLKQwLkgjkEYFZVaEYR5kyo1G3ZmnRRRXKbBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABQehooPQ0AW7D/kHW3/XJP5CrFV7D/kHW3/XJP5CrFABRRRQBx8n/H5df9d5P/QjUNxEjsHeYR44ySBU0n/H5c/9fEn/AKEar32l2OrQLbajZwXUAcP5cyBhkd8Hvya6VJxjdFKKloxgs0ZQyTFh6jkUv2Ef89CPoK5/4ciOHwXGq7UjS6ugB0CqJn/QCoh40vjpZ1/+wwfDwO7z/tH+kGLOPN8rbjb3xuzjml9Zqdx+wpdjpRZY4EjD8KBYjp5p/KqmrapqME1vDpelpdeahka5uJvJgRewLAElj2GKzYfF1xN4Y1XUo9LBvdLleK6tFm3DKYLFHA+YbTkce1H1ip3D2FLsbpsB/wA9D+VKLAcfvDx7VRvdfSKfRYrJBdNqsmYzuxthCb2k98DHHcmqV34mv5dUvrPRNLt7waeQlzJcXYhDSEZ8tODlgCMk4GTij6zU7h9XpLobRsQTzISfcUfYB/z1P5VjXHjK1TwpY67b27yfbpI4YYJHEe2RmK4djwoBByavaXqWrT3stnqukLassYljuLabzoJBnG3cQCGHpjpR9Yqdx+wpdi39h/6aN+VWI4/LQKDmpKwtb1+4sNUsdKsbOK4v7xHkTz5/JjVExnLYJJ5GABSlXnJWbGqNOGqRu4pMVhf8JHLY+H7/AFPW9NmsJLFissStvEuMbTE3G4MSAOBz1qvF4j1S1vtPh1zRo7KDUXEMEsVz5pjlIyqSDaME4IyMjIqOdl2idH5kYbaZED5xtLDP5U7FcZ400jTYrvRNTjsbdL+TWrVXuVQCRhkjBbr2H5VveINbbRra2EFq13e3k629rbhtodyCeW7KACSaOdhyxW5qnAOCwz6ZpcVxOmT6jP8AE9/7TsorWdNGwBDP5qOPO4IOAfbBHau2o52CimGKMVyHiDxpdaNd6pHBpUdzBpkME9xIbjYxSQkYUbTkgj1FSxeMXtIdcbW7FbSTSkilZIJvO8xZBlACQPmzxj1NHOwtE6rFGK5iHxHqltfafFrejRWUGouIoJYrrzTHIRlUkG0YJ5GRkZFSap4kv7XxKuhadpAvrl7QXSu04jRRvKnccHA47ZJNHOwtE6PFGK57UvEF/pNlpH2jSVkvtQuvszW8NxkI21iCGIAI+UZzjr7U7Stdvp9dm0XVtOis7tbcXUTQTmWOSPdtPJUEEHFHOwtE39pIJHQdaTFcpbX9+/jy0tdU0iG2nezma3nhvmkBQMuQybQMnI5q5qXiC+g8SR6Hp2lpdXMln9qV5J/LRQH2nccE4+gJ5/GjnYJRN/FGK5u28XouhavqGqWbWk+kStFdQRuJAWABXY3GQ24YyB1qJPEut21/pEGqaBDbR6nOIkZLzzDFlC2HG0fNgdsjrzRzsLROpxRilqjqupNpdsky6ffXpZtvl2cXmMOM5IyOKOdj5EXcUYrK0vW31S4eFtH1Wy2Ju33luI1bnGAcnmtWjnYciFCk9BmjafSuP1yK31Tx3Y6NqsrLpp097iKAStGtxMHAIJBGdq84981X8Ppcato/iHQtO1qYRWl8ba2vw3mukRCsUDE8kZZQ3bj0o52KyO4KkHGKTFefaK2kp46s7XwvczmGKCY6pHJLIUYcBDiQ5L7s5I7da78HI61pB3IlboLRRRVkBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU1/uN9DTqa/wBxvoaQjp9M/wCQVZ/9cE/9BFFGmf8AIKs/+uCf+giiuZgUofv3H/XeT+deefFqGW7tNPs4rO0uPtUyQAzthkYuD8vynqFOT2HrXocP37j/AK7yfzrI12zFzc2zlEYxDchYZ2n1HocE/nWlG3NqZzTa0PNvB9rI2patfvpWm26SXTxpJbnJQoAhVflHy5DHPr2rW1/xDFo6xWsXlSajc5EEMkgRV9Xc9kHr36Cuhi0wwptijjjUkthAAMk5J/Ekmql34bsr6XzbzTrK4kxt3zRK5x6ZIrvUoxjZMxdObd2jM8O2dpZ2Eogv4r+4mlM13cxuDvlbr0JwOwHYAVhaRBpN5e+I5tcjtJbuO+dZPtm3McAA8vG7ouOc/WuzstAttPVlsbO2tQ5ywhjCBseuBTbnw5Z3txHcXWn2c88f3JJYlZl/EijmhZah7OfY4/WH0zUbzVYo9K02drS3WO4u76UKFDIWVUGCehHPHPrVLS4ItUuvBQvoxcg6RKxEvzBiBH1Hf8a76Xw7aT3qXk1jayXKDCzPGCwH1xUqaLHE0LR28CGBCkRVADGp6geg9qi8W9ZDUJroefnSoJpfFmj29xBYW32u1kiRwBCHKKxUrkcMQAQK2/DVyPt+pWEmmWNnd2xjM0lgQYZdwO09AQRg8HnpXRS6DBcLOs1pbSLPjzg8YPmY6buOcUtnocOnQeRY2tvbRZzshUIM+uBVRcU73Bwm1scZpttpV7rfiRtfjtZbmO52gXe0+Xa7BsK7ui/eOR3rHsbdL/S/Bj31utwGvpY0a4QMzwBZDGDnqMAH8q9HvPDdnqEscl7p9ncvH9xpo1cr9MirEmlCV4mkhhZoTuiJA+Q4xlfTjik+V9Q5Jroc14wtLc+BtViFvH5cNo5jTYMJtXjA7YrF123sotH0GC1Wxh06a8i+1/L+5ZvLynmBSOC23r7Zr0J9PklRkdUZGBBDcgj0NVIfDlpbWT2UFhZx2r53wrGoRs+q4wacnCWzEoTS2OI1TTRZaZ4lmju9OXzNJdZLKxTYMgNiQjccHGR05pbnT7KwsPB97aW0UN095bI86KA7h0O4M3Vs+9dtbeGbK0tpLa20+zhgmBEsaRKFcehGORVhtIDrErQwlYmDRqVGEI6EemKXudx8s+xwWnaPpuoweLnvLOGZzf3KB3UMVAQHjP3Tk5yMUmiyvNqPgqWV2d20eYszHJJ2x8mu+TSvLEoSGFRKxaQAAbyepPqabHo0URhMdtboYEKQlUA8tTjKr6DgcD0o9zow5Z/ynB6LbR6BqemWUtvpeoRXMsgtNStyPtAYhmJk67uMgsDV7xTFZzeKPC8V6I2gaW4BWU/KT5fAPrzjjvXT2vhqysrl7m106zgnf70kUaqx/ECprnRYrxQLm1gnABAEqBsA9evrTTjZq4nTne9jgZJtK0jV/FDxW6Po6WcP2mC2wEM7EjAxgAkFc/malsI1s/H+kRx2OnWHm2U4khsZN2VG0rvwoHrjr3rt4NBtrazNnBZ20VqwIaFI1CHPXIxg5ptp4ds7AILSwtIPLJKeXGF25GDjipvHuiuWf8p53p3/ACKXhH/sN/8As8tXLm3j0PVbjUJoNM1WzutRUl2x9rt5WYKFXqGCnGBwRXdrocKRxRra2yxwv5kaBBhG5O4DHB5PPvUY8NWIv/t406yF5nPn+Uu/PrnGc07x6MXJPsc1463/AGTTI3aJLN9QjW6aYExbcHaHwR8pbbnmoFgtdFudT1KefTvIXTz9pstOj2F1B4cru4OCVzx1612sunGeJopoo5InGGRwCrD0IPWq1p4cs7CKSKz0+0t45OHWKNVDD3wOabcG73EoTtscFAiWni7wu9tp2naeJ1mXyrWUM7xmPcBJgAEZA55571b8JQaPPoEd5qaWT6mbp/tUtxt8xbjzDgEnkEfLgfSuztfDdlYhRaadZwBW3r5cSrhsEZ4HXBI/Gkbw5Zvf/b30+ya86+eYl3/njNJOKd7j5ZvocVp+j6bqMHi972zgnkN/cqGdQSoCAjGehz3HtWzosjT/AA/spZWLu2mAszHJJ8vrXSJpIjEoSGFRKxaQKAN5PUn1NS22hyG2EEEMKQIuxYxwoX0A6Yqk4LqS4ztqjziz0x7zQfC13byabNPbaaP9C1A/JIpC5cf3SMYyQRzXpXw+vbfUPBtndWtoLOFy22BcbUwcHGOCMjg9waq3Hgq2vIYYLnTLCaKAbYkeJSIx6KMcCuk0myNhZCAoiBT8qpwAPQVhWceSyZcE76ovUUUVxG4UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUHoaKD0NAFuw/5B1t/wBck/kKsVXsP+Qdbf8AXJP5CrFABRRRQBx8n/H5c/8AXxJ/6Earahc3trbCSw0/7dNuAMXnrFx65b+XvVmT/j8uf+viT/0I1U1LVbHR7UXOoT+TCWCBtjNye2ACe1dD+A1gc94NsdZsNKbRtV0dYIWa4ka5S7R/9Y5YLtHP8XX2rP8A7I8TL4RPg/7DbmPy/sY1T7QNn2fpu8v72/bxjpnnNdnp2qWesWKXun3C3FrISFkUEAkHB6+9W8cVibWOO1/RLx9c066j0iLW9NtrT7OLKWcJ5cuRiXDfK3ygD1FT+CdEvdIg1hNQsrW2W8vmuI4LZg0aoyKNo4HTBHSuqzml5NAuU5Hwp4ZvtJ1SeW/dJLeziNnpeGyVgLlyT6H7q/RKzbvwj9l8Qapdv4Vstet7+b7RE8kiJLA5ADId3VSRkEdOeK74DFLQHKc3Ppl9B4Tt7Ox0nSCUINxphGYHQklkRiMA85yRjNVPDWi3mn69dXiac+jaS1sEGnG584PLuz5gUZCYXjA6119FAWOd/wCE10znNtq//gruP/iKTxNZXd89sf7Fs9Z00IfNtZSqTI/Z0ZuOnBHB966TJ9aTvmgLdziLbwrqV14N1fSbhjZC6m8ywt3nM/2QLtZVZ+4LLnAzgHvVq4tte8RXmkx6lpcenW1hcpd3Di5WXz5EB2qgHIXJyScHFdbRQHKch4si1/Ubizt7DQllgs7+C7E7XqL5gTkqFPIPOM+1WNatNZ1Oz0nVILCKDVNOujcCxlnDCRdpRl8wDAJU5B7VoTeKdFtdWXS579UvWkWNYij8s2MDOMc5Hetf6igLXOV0201i58aSa5qGnLY2v9m/Zkj89ZZNwk3fNt46Z6Z/WrH/AAmul97bVx/3C7j/AOIraN/anURpxnX7YIvP8rnPl527vpnirWT6mgEjhfEfhvU9UfxU9tEhXUrG1httzhdzIxLA+nXvU2p+E7vVbjxQpdIYtRgtBayk5xJECfmHUDcBXZnmkPNAcpyM9tr3iO80lNR0yPTraxuUvLiQXKyedIgO1YwOQuTkk4NaSaZdf8J3Jq2xRaHTBbbtwzvEpbGPp3p8vinSYtWOlCeSS7DKjrDA8ixs3QOyghT06mtngUAkjD1/Tbq/1DQJrdAyWWoi4mywGE8t1z78kUp0y6PjyLVtq/Y00xrYtuGfMMobGPp3rbxzSUDscXcjxNJ4ts9YTw0pjtbaa32f2jGC+9lIbpx93p71rJpt4/jaHWHhVLf+yvs7jeCVlMgbHuAO9b9L1oFY4q+8Mz3Wl+MYbuaK0i1K4E8E7uNqhUTDN6DcnOe1U9Q1LWL3xB4Qg1LS4LNhfGQsl2splIifLIF6JznJ9RXfSRxyxPFKivG4KsjDIYHqCKy9N8M6JpF09zp2mW9tOy7fMReQvoM/dHsMCkDia3aqOp6XFqsEcUtxeQBG3BrW4aFjxjBKnke1XqKZRlaZoNvpVy80V5qM5ZNu26vHlUc5yAx4PvWrRRQFrGN4lsftunxoNBs9ZcSfLDduqLHwfnyQfYYHPNZ2neH9W0bwveJp09nHrVzN9qYrHi3DZUeUo7KEUKDjPeuqo6UEtHJiz1rW/FWkapfaUmlxaYspYtcrLJMzrt2Db0QdeevHFdVT85rOu9d0iwvEtLzVLK3uZMbYZZ1VjnpwTVwaREol+kplzcQWlvJcXM0cMEY3PJIwVVHqSahsNRsdUtjcafe293CDt3wSBxn04q1NMlwaLNFFFWQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFNf7jfQ06mv8Acb6GkI6fTP8AkFWf/XBP/QRRS6YP+JVZ/wDXBP8A0EUVzMCjB9+4/wCu8n86474hazLoj6BcLcSQQPqccdwUXcWjKuSuACTkgcDmuxg+/cf9d5P51xHxJZ4ZPDdyttcTpb6tFLIsERkYIFfJwPSkOOjLmleI7DWLm4tYPPiurdQ0lvcwNDIFPRtrDkH1FU5fG2hxXDo0tx9njm8iS8+zubdJM42mTG3rxnp71nWN5H4j8d/2npiTGxtNNktXu3haMSSO4IRQwBO3BJ9Caw4r5LT4cS+D5LG5Ou/Z5LIWaW7kSOxIEobG3Yc7i2fWqN+ZnoTapaprMWlFm+1SwNcKu3gopAJz06kUyPWbCV9SHnBF01tt08g2rH8ofOfTac1zF8w8N+JNC1HUml+xRaU9jLcpG0gWXKEbtoJAO081nPaXev6H49SztrhHvZka2SaIxtKBEmMA4PzbcfjzQHOzpLXxvot1PZxA3kRvpVjtWmtJEWbPQqSMEe/uKoW/ixbDXvEltfNeXK21zH5MVtbtKYo/KUkkKOBknr71mav4ksNbv/B0NlFdLJHq0Jljkt3j8khGGwkgDd14Gehro/DkMkfiXxW7xuiyX0RRiMBgIUGQe/ekK7ZPqHia0t/Df9s2azXkEkLSQNBCzg4UkFscqOOSelJ4b8Rrr9hbytZ3cEz26TOZLd0iyQMhGP3hnp7Vk+FbO4f4YNZCFkuHhuo1jcbTlnkCjn1yKt+C9bsrzQtO0xDOt9Z2ccdxDLA6GNlUKQSRjr70yr6nUVx+seIn0nx/ZWkr3L2kumSOLa3hMjPL5gAIAGfu59q6b+0rDnN9ajH/AE2X/GuU1i+j0r4k2Op3UVx9hGkSRvcRQtIkZMoI3FQcDj9aAkzoNP8AEWl6jpU+pw3Oy1tiy3BmUxtCV+8HU8giq2n+LtK1G9t7RFvIJLpS1sbm1eJZwBn5CwwTjnHXFYFjPfy6f4y8Q6XYSML11fT4J4SDN5cYUvsPOCegPJxWYt9FqOs+FnttR1vU2ivw1y9zbFIYGMbDGNihWycYGQBQLnZveMft+mXGnajZ6vfRifU7a2e1DL5Wxmwwxtzz9a6jUbJr+3+zpe3Vmd4Pm2rBX47ZIIx+Fcb4/wBWtmXTrFFuZLi11S2uJVjtZHCxq2SdwXBwPQ11LeItNXTE1QyT/ZHfYGFtJuzn+5t3fjigLq7M7wReXV54dZ7y6luZY7y4hEsuNxVJCq5wPQU648baJazSiSS4+zwy+TNeLbOYI3zgqZAMcHgnoKy/h9q1r9gk04i4ju3vLmdUktpEGxpCwO4qByDnGaxLq+XTor+PSJNTs9Sad2/4R+5tDcwXDludvy8K3XIbAzSBOyPUAQRkYwfSo7m5htLeS4uZUhgjXc8kjBVUepJ6URb/ACY96BH2jco6KcdKZexTTWU0du0KzMuEaaPzEB/2lyMj2zTLuZ1t4r8O3lzHbW2uadPcSttjijuFZmPoAD1rZ/CubstI16G+glurzRXhRwzrDpfluR7NvOD74ro+lAk31MnXDrL/AGO10cJD58hFxeMof7PGBnIUn5mJ4HXFUNCvtSj8R6loeoXiX4tYYriO5EQjZfM3fI4XjPy5BGODVjxb4gl0DSVktbdp7u4fyYP3bMkbHq77QTtA545PSqnhCfSYons7S6uLvUJibi7up7aSNp34y2WUADkAKOgFAup1Fc74v8RNoGk3UiwXSt9lkeO8SHzIopBwofuMnHbFdFVHUtKttWa1F3veO2mE6xBsI7DON4/iAPIHqBQNkGg60ut2gmjtruONUQieeExrPlclkB5I/Ada1cVR0nSoNGsfsVq8ht1dnRHbIjDHO1fRRngVeoGm7anKaqPEYXVL86tbaVa2gLWsZhSVZVVclpWPIycjAwRXV+FL+XVPDlnfzw+TLcwpK8X9wlQSP1rhPE19oOom9sPEmkXCXVsWWxKRSO04IBVomQYDZxx2IruPBo1AeFrAatn+0PITz92M78c5x39fekZTehvUtHakpWMAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKD0NFB6GgC3Yf8AIOtv+uSfyFWKr2H/ACDrb/rkn8hVigAooooA49/+Py5/6+JP/QjT0OGFMf8A4+7r/r4k/wDQjVXUE1GS3UaXc21vcBwS9xCZV2+mAy89O9bv4TSBz/w/lEXgsytnal1ds30Ezms7+2vE58Knxh9rtBbeX9rGlfZxj7PnP+tzu37efTPGK1/C/h7VtBt/sN1qVld2BMrmNLVkcs7Fj8xcjGSeMVT/AOEN1L+yT4dGsxjw+SV2eR/pIhznyt+7GO27GcVkaK5a8RavcQS6eINZtNKtLqMyeY8Pn3Eh4wqR+mDktz2qlpWv6zrPhLW3tJoZtV0y4khinWAotzsAYZjP3SwOMdjWpqegX51231nRb22tbmO1Nm0dzbmVDHu3DGGBBB/OpfDOg3Ohf2m9zf8A22a+vDdtJ5YT5ioBGB2yOKB2dytJ4ik1O78NxaS6hdSX7bOxXcY7ZVyw9iWZVz9aw9T8ZyS69qdnFr1ro8WnyeRGstk07XEgALFj0VATjA56mug8P+FYtB1HULpbhphcNtt4yMC2i3M5jHtvdj+XpTJNA1Ww1e+vdC1G2t4r9hLPb3VuZFWXABkQhgckAZB4oFZlCbxXqVx8P7LXLS1EdzO6R3DCFpRbLuKvKI/vMBjIHuPSr3hfUby+urgf29p+tWAjUrPEgimikzyjoOMY5B4Par13pWpT6Na28OuTRahbssn2vyl2zMOodBgFDnoCO1VdM0C8h8Qz69qVzaS3r2otljs4DEm3O7LEklmz3PQUDszoTXLeK9YvtNvdPgiu102wnDm41JrUziNhjamOi5yfmPHFWP7Y8SDI/wCES/H+04v8KtapYatPdQXml6ktrKkZjktrhDJBKDzkgEEMD3H5UDZknxHfaZ4Lv9Yup7HVTA2LSe0YBLgMQqbgOFO44IBpJbzxB4dvtKk1bUre/tb+6S0mSO1ERt5HB2lCCdy5GCD9altfBcR0PWNP1C4WSTVpTPO1rF5SRPgYMa5OMbQcnqetOi8Pave3unSa9q1vdwadIJoo4LbyzNKAQskhLHoCTgYGaBWYnjf/AI99BXJx/blp/wChGrfijU76z/s6x0too77Urr7PHNKm5YVClmfb3IAOBVXxF4f1zWbqHyNWsYLW2uo7qCN7NmcOnTc28AjOewqxqOhX+q6XZ/aNRhj1eyuftMF3DARGGGRgoWOVKkg80BqZGlwajbfE94tSvkvXGjZSZYBESvndGUEjOe4rt65zTdC1ODxI+u6lqFvcztZG1EFvAY0UbtwwSxP5+tL/AGv4l5x4S499Ti/woGiPxPqd1Y3lnBHrVppVvMrFpWh8+4kYYwqR9Mc5Lc9qh8L6zqHiLRtThN9Gt7Z3b2iX8dtgOAFIfym6HBwRVnVNC1GbX4Nc0m+t7W9W1NrIl1AZUKFt3GGBBz781L4Z0C40L+02ub/7bLfXhumk8sJ8xUAjA9xx7UhatnP+ArHVlk1WZtb3QLrFys8P2RB57AgFt2cjPHA6YpNfstYf4jaCsOuGHz4rswf6IjeQoVcryfnz6npXU6DozaLDfRtMJTdX015kLjaJDnb749ag13Q7u/1DTNU028htr7TzIE8+IyRukgAZSAQR0GCDTC2hR8Ua9c+GtI06FryE3t3MLf7ZPD8kYwS0hReuAOFHc1V8L+KJb7xFNpDapFq9ubb7RHeR2pgZGDANG46HqCCPetfU9Cu9W0yx86/jg1eylFxDdwRYQSDI+4TypUkEZq1pdrrSXDz6vqVtMNu1Le1t/LQHuxJJYn8QBQPW5zlpdeKNd1bXLa11a0sbWwv2gjf7IJZHG1Tt5IAAz15Jz2xV3Ur7WZvGUWh6fewWkL6b9pkmeASMrCTblQSBzx14Faei6K2k3WrztOJf7QvWugAuNmVVdvv93rTm0Zj4sTW/OG0WJtPK2/8ATTfuzn8MYoBI56LxVqGmeHvEEuoiK8vdHuvs0bxp5YuCwXyyw7cuAcelWJbzxB4evdKfVtRt7+2v7lbSZI7YRG3kcHaUIJ3LkYOeelWn8Iw3Vt4htru4Z4dYnE2UG1oSEUAg9yCoNRx+HdXvL7TpNd1a3u7fTpPOiigtvKM0oGFeQlj0yTgYGTQTZkMd54g8Qanq39lahb6daafcG0iV7UTG4lUAsWJI2rkgDHPU1VPizVNQ0Tw1daetvb3Wo35s7hZVLorKsgbHfG5MitKXw/q1nqOo3Ghapb2sOov5s8dxbmTypcYMkeGHJAHByMili8IRWlj4es7W4KxaRdfaS0gy052sGJ54JLk0BZjdLvtXtPFsuh6nexX8clkLyGdbcQsmH2MhAJBHIINdNWWdHLeK01vzxhbE2hi2+sgfdn8OmK1MUFoxNc1TU7G5iSwTSXRk3N9uvTA2c9htOR71Y0S+vr62le/TT1dXAUWN0Z1xjucDB9qmvdH0zUpFkvtOtLp0G1WmhVyB1wCRUlnptjpsbR2Nnb2qOdzLBEqBj6nAoDUtVxPiqxt9G0bWWtPD02oPqSSS3VydriIkYy2cvtUcgKDjBrta5m40HXlk1CHT9djjsr52ci5gMsluWGGEbbgMdwCMCgT2MjWYUh8KeE53aTVNIspoJLuSNC/mxiMhZCvUqG2sRg0/RNSsL/4l3kmkRstnPpatM4hMaTSLJhWAIGcAkZx2x2rq7fTX0zw/DpmlTJC9tAsMEsy7wMADLAEZ6eoqro+iS6fc3V/f376hqd0FWW4ZAiqi52oij7qjJ9SSeacdyGma9FFFdBkFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFI33T9D/KlpH+6foaQHT6X/yCbL/rgn/oIoo0z/kE2f8A1wT/ANBFFcrJKMP37j/rvJ/Os3VrnyJ4/lJyvY4rSh+/cf8AXeT+dcV8Tr+60/RJpbO2klleBow6SBfLZvlVueTyR05q6dubUmbkl7ppnUMn7hP40v2/jGw/nXD+H7jUJvEN/HPYzxQW0EFp89yrhGVNxyAeWbcvPtzUmqaxqFr4w06xt7C4nga3mkZUdB5uNnPzEY289fXjNdahBq9jB1617XOz/tED/lmR+NMuLpbm2lgYSqsiFC0chRgCMZB7H3rm7jXZRqNxZ6fpVxfyWgU3LJIiKhIzsBY/M2MHA9etQyeLLUnS1s7S4vH1KKSW3WMBTlMZVskbcZ5PbBo9nTBV63cvWuj7NStL3UNU1HU2ssm1jumTbExGN52qNzYyMmt/+0Mj/Vnj/arkD4shi0nVLy6sZ4J9MZVubUsrMN2NpBHBBBz+FTw+IX/tm1sLvS7mzW8DfZZpWUiQqMlWAJKnHODRyUw9vW7nUm/OclDk+rc0n9oEjBjJHu1cPY61Y6bpUs1vBezNcarJaxxSyhmeYsQQGY4VeCeeldDp9zc3MJa7sZLKZW2mN3V88dQVPI/KhU4dhSxFVdRx0fQM5Ph7Tjnnm2T/AArVW/EahVjKqOAA3ArzvStYvLF/ETQ6Vd6gkWqTNIY5FGxcLwu4/MR12it+TW5J7KzudJ0241FLuPzYyjLGirx95mPB56c9DQoU30G69ZdTpft4J+4fzpf7QyBmMn6tXAatq6ato+i3dussDHWoIZYn4ZGVyGU49xT4tVi0jWvGF9eFzb2xtjtXk8xgAAe5IpclPsP21Xud7/aOP4P1pP7R+bIRs/71YWn3l5dlxd6XLYlQCpaVJFcHPQqevtT9R0y31S1FvcmYIGDZimaNs/VSD3qvZQtdIn6zVT1ZtG/YjHlNj60fbyONjf8AfWK830Hw9ZXWr63HPNqDrZXwjgBv5vlXYrY+9zye9P03WbvT38SSR6Xd38cOpzPI6SKojTavC7j8xGCcCoUIdUW69Toz0T7cP+eZ/Oj7ev8AzzP51y0viSJ/sMem2kt/cXsH2mKJGVAsXHzOzcKMnH1q9peppqcc5EElvPbymGeCXG6NwAcZHBBBBBHUGrVOn0J+sVluzb+3j+4fzo+3D+5+tcpqPiSXS2mmn0a8GnQSBJrzcg2gkDcEzkrk9akutfePVbnTLPS7i8uoY45TsdUQq2cZZiMdOnejkpIPrFbudR9vx0Ug/Wg6jnGYyfq1cgPFsEtjYTW1nPNdXsz28VoxVGEiZ3hiTgYweee1WxrbxXOnW97p89pJeyPCu9lYI6jcASDzuAOPpRyUw9vW7nR/bx/zy/8AHqT7fz/qj+dc3aeI7K71TVLIB4zpw3SSPgK453Ff90jB96rReKftaWS2OmXFxd3VsLv7P5ip5UROAzsTgZ7ClyUw9vX7nXfbx/zy/wDHqPt4/wCeX/j1cjN4vs7fTobuW1ulZ737DJAFBkil54IB56DGOuRWtptzdX1v5l3YSWEm8jyZJFckdjlTjmmqdN7CeIrLdmyNQ4+4R7ZrU0e4E0coC42kd68w0nXbKy8P2JsLW9uHvLuaC3gmmBdnDMWy7cAcGvQPCc89xZzvdWUlpLvCtE7q3TuCvBFZzjBR0LjVqOVpM6GiiiuU3CiiigAooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUHoaKD0NAFuw/5B1t/wBck/kKsVXsP+Qdbf8AXJP5CrFABRRRQBx7/wDH3df9fEn/AKEaytb1BNLs5r2YuIYIjI4QZJx6e9ar/wDH1df9fEn/AKEa89+JjWrvp1pcPqYa5ljQpaCQo0Yfc+QowWwDgde/auym0ldkz2Oji1VZNTksFaUTxwpO/oFYkAfX5TVXVvEY0iREks9UuQyFy9pbmRVA9SOlcx4Oezm8Rarcwy6u7NJ5URuxKVMaKoO4sMbgxbAPIrsrzixuD/0yb+Rrqj70b2OVtp2uY9h43ttRj86Kz1dLfyXnE8tsVjKqpJw2cduK2rDURqOn217A8nk3ESypu4O1hkZ965nQT/xay3/7Bb/+gGsWzs7jSvB/hzV4NTvzclrRHRpiYmjcquzZ90AAjnrx1qE7WbXQrV9T0jzZP+ejfnR5sneRvzrz+W5vNZ13WVmg12SGzuTa266bOsaRkKDub5huYk55yMYrqPDc2o3GgWravGUvgCsoOMtgkBuOMkYJxVRnCTtYmXMupriWT/no350CWUkfOfzri7S2udfh1nUpNVvbee3up4LVIZyiW4i4BKDhicZOc9aoWZvtaTwnZy6pewx3WmSy3LwzFZJCNhHzdjk9euM+tHPH+Udpdz0PzJP775+tKJpP77/nXGWFjJr+o6tHc6jqUUWnTiyt44LhoyNqKTKxH3mJPfj2qjZ6nqOsjQ9JuL6VBNNdpc3UDeW9wsDYUBh03ZBJHPBo549gtLuegebJn/WN+dL5soP32rhtde88OaZNbWWqXNwbu7ghhRnEk9sr8NhmPOcHbu6HvUmmLqNn4htI7a11pLCZJFul1KdZQGAyrqdxYHPBA45oc4p25QtLudr5suOXf8TSebJ/fb86870rT9UvvAp1uLWNSk1Z4WkiQzny/kfIXaOpIXBJ65Nbdhq7eIPENjJZyyLYW9it1Oqtw8ko+RG9cAMfxFNSj2F73c6nzZezt+dQXuopp1nNeXVw0cEKl5HJJ2isPxTFqcltZiwW4lhWfddW9rOIZpUweEbI6HBIBBNcxqUtveeBfEdsl5qqvbIshs7/AHLNb+iliSXQ8nknpSlKKdrDXM+p6Us0jqGDtyM9aXzZR/y0b86402z33iKDQWv72Kws9PS4ZUuWWWdmYj5nHzbVx0z1NZl/falZafrOlQ6jcF7LULSO3u2fMipKVO1j/Fjkc9QeaOaK1aC0u56L5sv/AD0b86POl/56P+dcFqkdxpmr6f4fgm1u6tXilu52iuMzykEKF3kghR1IBzyK1fDB1KK81C1uIb5bBdj2v251eZcg7kJBJIBAIJOeaFKLdrA+ZK9zp/Nlz/rG/OjzZezt+dclrcFxqfjGx0v+0b21tJLCWaVbWXYXKuoHPbr/AErKE93q2rasssevyw2Vx9jtv7PnCLHtUfO3zAs5JzzkUOUV0EuZ9Tu59Sjtrm2t5rkrNdMUhTnLkDcfyAzVjzZf+ejfnXnTwXupX/gyTWHuYb5pLmOby5dmdqEhvlOASAM49cVuaXe3Elj4okluHYwX1ysRZjmNQgIA9AOaFKLew3zdzqBLL/z0f86XzJT/ABt+deeWovdbfwtZS6rfQRXOkNNctBMVeRhswd3rk9evX1q3ZadPq+v+ILO51bUltbOSGO3SG5ZCrGJSWJHJ9cdMknFJST+yFpdzuBNKf+WjY+tJ50o/5aN+debJrep6hpPhezke+la8jmku2smCTzCI4ABJGM8EkHNTz6nrOh6XrjxQX8VmlvG1o+oyLJJDIzBGGdxJUAhhn0NHPDsFpdzuLzVksZ7OGZ5N15N5EW0cbtpbn8AamnvltLaS4uJ/LgiUvI7NgKo6k1xeo6L/AGZrHhmRNSvrjN+FlW5nMgd/Lf5xn7p68DjnpV/4gu8fg65ZEVx50AZX+6w81chvY96aas3YNbrU0LDxdZ6hew2i/bbeS4QvbfaoWiW4UckoT145x1xV7UtXTS7MXVzJKIjIkfycnLMFH6kVz0ravpOvaSNSvLbUYL2ZoQv2VYzbSbCQ0ZGTjAIOeaseNv8AkXV/6/Lb/wBHJQrcrdgbd1qdL5soP+sf86POkP8AG351yNtay+JtY1t7q/vreKzuzaW8NrcGLZhQd5x94ktxnIwOlZsesSX2h6ZFfahfNcG4nheLTYz516I2KhsgjaOhJ4zS549gtLuegebID/rG/Ok86T++35157p41W5bxNo9rLqdq0NvDPZJeT75onIY43An5WKjgnoa0bbXpdd1HSpbR5Et7ewOoXiIcbnIKpEfxDnHsKftIdgtLudl5kpGfMb86TzJf77fnXmFrc61e+H49Ygi16TV5VE6TecgtDk52eXvxsxx0zWrrg1JtWuri8g1mSwaGPyG0m4w1o235t8YILHPOTkY7UKpFrRC5X3O7EshYDecZrU7Cub0O6jvdHsrmK7N4jxri4ZNhk7Elex45FdJ7VnWtZWNqLd2FFFFYnQFFFFABRRRQAUUUUAFFFFABTX+430NOpr/dP0NIDqNM/wCQVZ/9cE/9BFFGl/8AIJs/+uCf+giiuViKMP37j/rvJ/OuI+Iuif23PpMW2UolwhmKztGBEDubgEZOQoHcZyK7eH79x/13k/nVHVIElmjZ85C4q4NJ3ZLi5aRPPfDOgDSvtt1KJ1nnuZTte5eQeXuwnUkZ2qvPXnFGsW9/H4m0zVbTT2vYoYJoJEjkVGUvtIb5iBj5ea7P7FD6H86PscHdT+ddPtYWsZfVqt7nnk+iyWWu6jdyaANWt76RZkZJEV4n2hWVtxAK8ZBHvVpNFuV13w9dRafFZ29nbXCzRRSArEzgYA6Z5zyBXc/ZID/Cfzo+xw/3T+dL2lMf1aqedax4f1K7j8WrBAHOoC2+zEyKN+wDd346d629Vsbi61jw/PCgaK0uXkmJONqmJlB9+SK6n7HD/dP50otIMfdP50e1ph9Wqnn9to89v4evLS/0T+0Fm1GeY2wlUN5bOWVlyQM9OMg81o+F7K8srS6S6WeK3actaW9xP50kMWAMFsnvk4ycDvXX/Y4PQ/nSfY4QOFP501VghPDVWef2seuaSNaig0c3Jvr6aa3kFwgVQwABcE5A4zxmo10bVNLh0fTfKur3TLez8qRLO4ELGfP3mJIJTGcYP4V6KLSH0P50fY4OoB/Ol7SmP6tVPNbXw3qcOi2tq1siyRa8L0oJgwEO8tnceTgH61pjTLqLUvE1xLpqXkF8YPKgZ1AmVU2sDngfjXbizg9D+dH2OH0/Wj2lMPq9VnnukWep6QNSutP0q5is/IUWul3N2rEygncVOWCLjAxn8q6+MsY0LrtbAJXOcH0zWmLOL0P50Czh/unP1pqtBEvC1JdjktB065s9U12eeMLHd3omhO4HcuxRn25BqPSdLu7W28QJLEA15ezywDcDuVlAB9uhrsfscPo350hs4fQ/nQqsB/Vah5nD4bu7OLRbyfSU1H7PpiWV1ab1DoQchkJIU85BGa6fRLZYLaQ/2RHpnmSbjCjqzNwAGYrxn2yeAOa6b7HDj7p/Ok+yQ/3T+dJVIIHhqrPL9Y0XW9X03VrO5srme9ld/InN4FthHuBUKgbrgY+ZevU10tjYXMHi3Ub94x9mmtLeONwRyyb9wx+IrqEjs5JpYUkRpYseZGrgsmemR2zUn2KA9VP50e0p7j+r1dtDzyPRpYdDks9Q0FtSSTULify45lV0VnJR1yRzg+oIo/sTV28JNEVke/tr0Xmnwzzb3jVWBSNn7nG4de+M16GLOH+6fzo+xwk/dP50e1ph9Xqnmmo+FtSOjabFZbPtcqSW2ovuA/dzMHlYeuGzj61d1fQzDr8epxaOmp2bWi2r26sqyRbCSrLuIBGDgjNdvZ/YNQtUurSVZoHJCyIcgkEg/qDU5soeuD+dL2lMPq9U4KbR7ia20g2ukRaeYdVjupoElU4QAgsSOC3TgZrrAeR6VcuI7O1tpLi4by4YlLu7Nwqjqap6XqOi63539mXsV15OBJ5bE7c9M/lVKtBEvC1GcVZaLPaeDbfTtS0E6iFuZpHhjmVXQF2KspJHOD2IIzXbfDizv7HR7uK+MoBuWe3inl82SKE/dRm7kc9z1xmo7nV9AtNSGn3OowRXpZVELuQxLfdH45rctj9j3iL+Lrnms5zg1ZFxw9RSuzaorM+3Teq/lS/bpvUVgdCpM0qKzfts3t+VJ9un9vyoH7JmnRWb9um9vyqCHWkuZ7iCCeKSW3YJMi9YyRkA/gc0C9kzZorN+3Te35Ufbpvb8qA9kzSorM+3Te35Ufbpvb8qB+yZp0Vj3WsCytZbq6ljht4VLySPwFA7mpF1CV0V1KlWGQQOooD2TNSisz7dPjPy/lS/bpvb8qA9kzSorN+3Tf7P5VBZ60moWqXVnPFPA5IWROQcEg/qDQL2bNmis37bN/s/lUVxqptbeW4nkSOGJS7uw4UDqaA9kzXornNL8VWOteb/AGbf290YceYI+duemfyqO58ZaXZ6kNOudTtYrxmVBC/DEt90dO+aA9mzp6KzPts2ew/CpIbuV5lUkYJx0pA6bSuX6D0ooPQ0EFuw/wCQda/9ck/kKsVXsP8AkHWv/XJP5CrFAgooooA49/8Aj6uv+viT/wBCNZOqacl5qNhduHL2ZZ41X7u5lK5P4E4+taz/APH3df8AXxJ/6EaqagNT+zD+yfsf2ksM/a92zbzn7vOeldUZcqTHy8ysZen6Yum232eESspkklZn6lnYsf1NWJIGlheNkbDqVOB2NJ4U1m517QI7+7iiinM0sTLDnb8jlcjPPOK0vt9oLz7H9st/tWM+R5q7/wDvnOav632RH1RP7RhWmiQ2mhppEaS/ZUhMHP3ipBB59eaifw7bSaPaaUyzC2tDEY8H5v3ZBXJx7DNdLdXltYw+bd3MNvFnG+Zwi59Mmj7Vb/Z1uPtEPkNjEvmDac8DB6c0vrXSxX1Vdzk73wrFd3s95BdahYTXIAufscmwS4GMkEHnHGRg1pWGnRaZYwWVnAYreFdqIATj8e5rclmih2+bKkYZgql2Ayx6Ae9R3F7aWZQXV3BAZDhBLKqlj7ZPNCxNtkDwq6s8z1fS7hr7VU/sHWvNumO1bC422t38uA0vI2Hs3rjvXQ6N4aFhaaM1wrm806z+zZQ/KdwXd+qjFdi0iRRmR3VEUEszHAUe57VDa3lrfRmS0uYbiMHBeGQOM/UVKr63sP6strnK3vheK7vpryG51GxmuFCXBs5dglAGASMHkDjIwaWXwnYPp1nZQQTWq2TbrWW3YrJEehIbvnnOc5712HNMeaKJkWSVEZyQgZgCxAzx68c1X1n+6H1VLqcevhCxNnewXKXN296yvPczuTKxX7hDDG3b2x0p9j4YjtL9b+a5v766jjMUT3cm/wApT12gADJ7nrXVW13bXqM9pcw3Cq21jDIHAPocd6SG+s7mWSK3u7eaWL/WJHKrMn1AORR9YX8ofVV/Mc5BZp4a0BYLKyvLmK1X5IY13StlskDoD1ql4P0GTRtHkMlo1vPdzvcSQ5z5QY/Kmf8AZXA+ua3rzxVo9jrVtpdxewrPOJCWMqBYtgBIck/KTnitP7VbGOGQXEJSY4iYSDEhPQKe/wCFH1nVOwvqq2uc9q2hRautu0puIJ7aTzILiBtkkbYwcHHQjgg8GqsfhOzFjqFtP9qun1BQl1PO+ZHAGAAQAAB2AFdd5kfneTvTzdu7ZuG7b0zjrjNQTalY26s09/axKr+WWkmVQG67eT19utDxN90H1VLqctJ4UikS0YXmpR3lqhijvUl/fMhOSrnGGHTqKevhOyXTWstlw6yXC3MsruWklkVgwZmxzyB+FdXNeW1srGe6giCpvYySBcL0yc9venQzR3MKTQSpLE3KvGwZWHsR1pfWF/KH1VfzHNaroMOrPBNIbmC6t2LQXNuxSSPP3gD0we4INGlaDDpLXEkX2me4uWDT3Fw2+SQgYGTjoB0A4roIb60uZpIYLy3mlj++kcqsyfUA8UXF9aWrpHNdwRSyDMSSyhS59gev4U/rOt+UPqqtuYj6TG+sRaoyy/aY4GgXH3djEE8euQKoXvhaG6vri7gutRsJblQtx9jl2CXAxkjB+bHGRg1ueFtYl17wxYarPGkUtyhZo0JKghiOM89q1846UPE3+yCwq7nGz+FLGWz062hS4tF05t1s9u5V04wRkg5yOvrUVz4NtLq7vJTNqEUN6266toZisUzYwSRjIyOuCM967jBJ+6c00ZFJ4hP7I/qq7nK2nh21sZrCWFJ91jam0h3HP7s7evHJ+Uc1PaaRFZ3t9dxJKJb51klzyMqu0YHbit66uobK0murmRYoIUMkjt0VQMk/lWZomr3+sqbmTRpbHT3Tfby3Eq+ZKD0JjH3Rjnk01ibfZE8Mv5jEPhGw/suysYjdQ/YWZ7aeKQiWJiSSQ3vk8HipLXwtZwRXq3AnvXvl2XMt0293ToF4AAA7AAV1tFa+1XYz9j5nFweD7aO6sriW61O5ayfdbLPPuWPjGAMc8dzk8Dmti8sItQs5rO6g8y3mUpIhB5BrbxSjihVUugvY+ZyFj4Xgs72C7luNQvZbZSlt9rl3iEHg7eBzjjJycVe1HSotVtPs10snliRJPk4OVYMP1AroaKFVSWwew8zkb3wxDd3897FdahYzXKhbj7JLsEuBgEjB5xxkYNMk8JWRh09LT7TYNp6sltJavtZVb7wOQQ2epz3rsaM5o9ouwex8zldL8O2+k39xewNdSXFyqrPJNIXMhUnDH35x6YxxT9K8PWejy3r2ULq15MZpd3PJ7D0Xk8e5rp6XNCqpdA9j5nDf8IRYCP7MJr8aaZPMOnib9wTnOMYztzztzirV34Xiub+a+gu9QsZ7hQk/2SQIJQBgEgg8gcZGDXXUuaXtV2D2PmczbaDb2i6bHaCe3h0/IihjchGBGPnH8Xrz35rpM0pzRUznzLYuEOUKKKKg0CiiigAooooAKKKKACiiigApr/cb6GnU1/uN9DSEdTpn/IJs/wDrgn/oIoo0z/kE2f8A1wT/ANBFFcrAoQ/fuP8ArvJ/Ouf8Z3t1p+nCe0uLG2YMqvcXz4jhQnlscbj6LkZJroIfv3H/AF3k/nXM+NdEuNaisTZ3EUN3ZXK3UPnx74nZcgBwOcYPbkHFBUNzA8NeJZr7xNcaNNqFrqca2q3UV3BCYSPm2lGUkjPQgisyPxH4pl8EN4pFzpoitw7vafZiTMiOVb593ykgcYGK3tM0TWE8VnXtWv7OWRrL7L5FtCyrH84YYJJJ75z61FD4Tki+Hlx4ZN3GZZYpYxPtO0b3Zgce2aZrqL4n8Q3Wny6VHBcQafZ3gZptRuYTKkGACqYGAC2TyxxwahfXNYtPDE2ozXGnXYgu4gt3afMlxbl1DNtBO1gGPc9K09R07Vx9jl0nUIEaCLypba6jLQTjA5OOQwxweevSqGm+EZINH123up7cT6yWaRLWMpBCxTaNi9fcnuaLj1Kdx4vvjcazbW8cPmRXsFvYMUyHVpfKcnnnDK/6VHqfjF217UtPttd0nSItPYRBr2PzGuJMZPGRtQZAzyc5q9beDDDceG5nvFdtLRvtPyf8fLnLBvbDkt+NSzaDq1hrV9f6Hd2HlagyyXFtfQswSQDbvRlOeQBlT6daLi94qTeMrib4WzeKLWKKO7jiJKEbk3rJsbHqOuPqK2dIj8QNc/adVvLL7PJHlbO3gIMTHBGZCfmwMjoKh1nQbvWPBd1ok1/G11cRBHufJ2KW3A52DoOMVvIoWNVz0AFBSTuLWPrF9rNnPEum6fYXMbLljc3vkEHPQDac1sYHrWfqOgaPrEiSalplneSRjajTxByB1wM0XKY3RrzUbuGVtStLS2kVgFW2uvPBGO5wMfStLNU9O0fTNIjePTbG3s0kO51gjCBj6nFXcD1ouJHC+IYNbPj3w8sGrQxJMbryVNrkRgRgkN8w3+3TFafiTX7rw1olkJri1e/up1tluZk8uFCckyMoPQAdAeTirPiHRr2+vtL1PS7m3ivtOeQotyjNHIrrtZTtOR25pNV0K81rRrNbi8ht9Ws5xcw3EEZMayDIxtJyVIJBBPei5OpmeHfFMl74mOjSarYatHJbGeK6tIzGUKkAo65I7gg+xrOj8ReKZfBT+KRcaasVsJHe0NuSZkRyrZfd8rYHGBj866vSbbW1umm1W508RhNq29lAwBOfvlmOfbA4+tZ0PhOWP4eXHhk3cZlliljE4Q7RvdmBx7ZoDUTUNY1abxbZ6PpclrBFc6a120s8RkaMhwOACM8EDBI9fapdD1HV76LW9Nup7b+09On8hLpIT5b7kDqxTPbPIz2qymguniqy1jz0KW+mmyMe05Ylg27PpxTrTSLqwvdfvLe4hM2oyrLCJEO2MrGEG7B55GeMUXDUz/DFxqH/AAkevWOpvZTXFsLcm4trbyTJuQn5uTnGMDmq2t+Ib+38STaZ/alholvHCj29xfQF1u3bOQGJCgLgAjrzUunaH4ls/EN7qkl/pLi+MIuEW3kGFjGPl+bgkE9c1d1PS9de+uXsL2wubK6VQ1nqcLSJCQMEpt6g91PfvRcNbGppMt7PpNtJqMUEd4yZlED74yfVT6Hr+NXe4rntM0+58JeGLHTrO2m1aSHKsVkSLGSWyNxwFBOAOwxV3TdS1G7uvLu9DnsYgu7zXuI5AT6YUk//AKqLj6HBeH7zxFpHw6TWLWewexs2nlNk0B3yxiVi2ZM8N1xgY4Fdbr+sXNumnvb6pp2mWlypdrm7HmOSQCqpHkbjg5J7elY8Pg7xBF4d/wCEb/tix/sqYuJ5Ft2E6xsxZkQ52nOSMkZGa19W0HUP7bsdX0WezjntbVrPybyNnj8skHKlSCGGPxFAtRfB+vz65a6hHcyW88lldm3+0W6FUnXaGDBTypwcEe1Q6Hx4/wDFf+7Z/wDotqn0TQ9V0mLWpZdQtrq91Cf7QkphKIr7AuCoP3eB0OcVU03RPEtl4gvdUkvtJf7cYftCLbyDCxgj5Pm4JBPXNFw6D/HuP7J0wgc/2xZ8/wDbQVo+LdVvNG0OS8soBJKJEQuyM6wqzANIyryQo5wKoeJ9E13XJI4ba906GzhuYrmMSQO0m6M7sEhsYJ9ulX7mz1y70eNf7UgtdUjmEolt4WMTgH7jKxyVI4PNAdyp4c1HUL68kDarpesacYwy3dnhHjkzjYyBjwRyDx0wa6QYrmtI0C/j8SSa9qj6elwbY2wisImVXBYNudmOWPHHpXS5+lBS8zkPHfip9D0fULe2tdTF2bUvFdwWpeKJuxZ+gxitfw/r8euWzMllqNs0SpuN7bGLeSOq569Kt6vpset6Pd6XPJIkN1EYnaMjcAfTNXETy4kjByEUKPwoFrccK4LToddn8WeLl0m+srRFuoWJntzKXfyVwvUbV9+T+Vd50rk20HxBY6zrGoaRqenoupSI5juoGbytqBdwKnk8dDx0oCSIl8X3174c0eWztYY9W1O6ay2SZMcMiFvMcjOSBsJAzzkVe0/UNVsvFEWh6vNBdpdWzXFtdQwmI5QgOjLkj+IEEfSoD4O8jw5pdjY35iv9Mn+1Q3cibg8pJL71zyrbmyM96tabo+pN4gGua1dWslzFAbe2gtEYRRKxBZsscljgDtgCgXvGdH4n1F/hhd+IiIPt0UUzqNnyZSRlHGfQVb1TVdUn16w0LSZbe3mltDeXN1NEZPLjyFCquQCSxPU8AVmS+Dtb/wCEf1Dw5barYx6TOZTE7QMZ1V2LbDztwCevXHpVjxME0rWNK1WHV7LT9QW3e1H9oI32eeLglS4xtYHkc889aA1M/VtS1O98HeObPVXtnl09DCj28ZRWBjVskEnnn8K0H1HX9Gn0GW8uLKexv547R7aKAq0JdSVIcn5sY5yBWZoWnXXiXQvGKteRSjVrgxw3iQlYpMRqpZFzkqCMA55xXV6toUmpQ6Kizqn9n3kNyxKk7wgIwPrmglJvU5/xFBrX/Cf+Hkt9XhjSX7UYUNruEYEYJDfMN/t0xXZ2qXEdtGl1Ms84GHkSPYGPsuTj86yPEOjXt9e6XqemXNvFf6c8hRblGaORXXaynacjtgirUt1qtnpkDvYJqF6TiZLSQRIOvI8w9Og60FrRmoOory7w/eeItI+HS6zaz2DWNm00psmhO+WISsXzJnhuuOMcCu903UdRu7ry7vQ5rGMKWEr3Ebgn0wpJ/wD1Vy8Hg3X4vDp8NnWLL+ypmcTyC3YTrGzFmRDnac5IyeRmgT8jY1/WLi3TT5LbVNO020uV3vc3Y8xzwCqxx5GTg5J7elHg/X59ct9RiupLeeWxujB9pt0KJMpUMG2nkHBwRntRq2gX/wDbVhq2iz2kdxaWzWnk3sbNGYyQcqVIKsMde4p2h6HqmlLrU09/a3V7qE4uFk8koivsC4Kg/dGB3zigFe5Do3HxB8UgcfurL/0W1M8egDSdMOOf7Ys//Rgpun6J4ltPEN5qkl/pDfbvJWdFt5BhYwR8vzcEgnrmpfE+ia3rkkUVtfadDZw3MNzGJYXaTdGd2CQ2ME+3SgOhe8WareaNocl5ZQCSQSIhdkZ1hVmAaRlXkhRzgVB4U1LUL7VGzqul6xp2wFbuzwjxyZ+4yAngjkHjpVi5stcu9IRRqtva6pHKJBLbxHynAP3GVjkqRwear6D4f1CPxWNe1V9PS4MH2YRafCyq4LBtzsxyxGMD0pClc7vvQelKRSHoaDnLdh/yDrX/AK5J/IVYqvYf8g62/wCuSfyFWKACiiigDj3/AOPu69riT/0I0u9U2l2VRnGWIFI//H1df9fEn/oRqlqVlpuowC31O1guYQwYRzxh1yO+D9a6LNx0LjJLVnP+AZP+KLcQshn+03hRNwznzXxxXNC00U/B5tTZIv7W8oym7wPtP27d03fe3b+MentXc6dpHh3Sbn7Tp+mWVrPtK+ZDAFbB7ZFO/sjw8dT/ALT/ALMs/t2/f5/kjdu/vZ9ffrWapy7D9pDuYmsst94r0qxk0/T59TXTjO0upOTDEpYBgkfRnJHXjA71n+D9Mg1rw54s0SR7Y2smpTwx/ZP9VFlVIMYPQBuRXX6lp2iayI11OxtrwRklPOjDbfpUtpFpmnI6WVvBbrIdziGMIGOMZOPYAfhRyS7D54X3OS0S9l8V6vo8F2vz6HE02oIRx9tBMSj8Arv/AMCWs6Ozv9W8WeKEmtNAuZknEWzVd5kS32DZsAyAhyTkd816DB9gtpZ5IIYonuH8yZkTBkbGMt6nFVNR0rQdYmjm1LTrW6kQbVeWIMQPTPp7Uckuwc8P5jlrjS45Phzoul3+uWLyfaIlt7g5ltrgq5KRN6oQAvJ7VoeH/ItfGV1Z3GjWdhqjWKytJp026CaMPjJTA2uD6jOM810U0OmXGnmwmtrd7MqENu0QKYHQbcYqHTbDRdISRdOs4LTzf9YYUwW9MnqaOSXYfPC+5q5rkPGtlb6jqvhW0uk3wS6iwdMkBgInODjseh9qnHh6DOf+Ek8Rf+B3/wBjW3J9hme3eWNJHt23xO65KNjGQexwSKOSXYbqwa3MHxdZpongHWhoVpFZSGHJFrGEOMgM3yjqFzz7ViWWi3S33h28tz4XsIIZV8qTT2cSXUZU5j5Hz5HPPcZrvjdQEEFsg9QRWbZaP4e028+2WWl2dvcHP7yOEAjPXHHGfaj2cuwvaQ7mHr+m6afiN4aM9jZ7LiO8EhkiXEr7FxuyOT1xmpvGMcFvL4TMaxRWsOtQjK4VEBVwPYDNb2o2mk6vAsGo2kF1Erb1WWPdtb1Hoaa9noz6UNKeytzp4UKLYxDy8A56fXmj2cuwOpDuY6SxSfFt0SRHePRMOFIJU+dnBrP8NaDpOo6/4rur7Tre6lXVXjUzxhwq7EPAPAJJ5I54FdPYafoullTYWFtbMqGMNFEFO0nJGevXmrNutjbSStbxxxtcSeZKUTG9zgbj6nAHPtR7OXYOeD6nNXun2d98U7ZLy2injj0YsqSKGUHzsZweDwe9Yd0s2meFviBbaSpgWC8/dJDx5SNHGZNoHTgseK9H+y232wXnkR/aRH5Qm2/Nsznbn0zzikhtLaCSeSGCNHuG3zMqgGRsYy3qcAClysp2OBsdDuUvfDt3AfC9hBDKvky2DOsl1GVOY+R8+RzzzkZq1pllpWo+IPGEmuwWkt1Fc+X/AKUFJitfLBTbu+6OpyO9dNZeHdE068+12Wk2dvcc/vI4gCM9cemfanX2gaPqd5Fd32mWlzcRDCSSxBmAHIGfTNHKwMn4b/8AJOtFHbyW/wDQ2rqDyD16VFbW1vZWyW1rBHBBGMJHGuFXvwKlzRysaaseePocu8n/AIRXXm56jxCR/wC1K9DQbY1BBBAHBOcUuaN1HKxppHNfEOOWX4f6ysIYt5AJ2jJ2hgW/QGskwW2j6/4Zl0jVLq5OpSMlwkt20wuIfLLGXaSQMEDlcDnFd2SCMEZB4NZthoGjaVcvcadpdpazSDDvFEFJGc49h7CjlZMmnqaJpKKK3RiwooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTX+430NOpr/cb6GkI6nTP+QTZ/wDXBP8A0EUUaZ/yCbP/AK4J/wCgiiuZgUIfv3H/AF3k/nT2RXOWUH6imQ/fuP8ArvJ/OpaQhnlR/wDPNfyo8mL/AJ5p+VPooHdjPJj/AOeaflR5MX/PJPyp9FArsZ5MX/PJPyo8mL/nmn5U+igLsZ5MX/PJPyo8mL/nkn5U+igLsZ5MX/PJPyo8mL/nkn5U+igLsZ5MX/PJPyo8mL/nkn5U+igd2M8mL/nmn5UeTF/zyT8qfRQK7GeTF/zyT8qPJi/55J+VPooHdjPJi/55J+VHkxf88k/Kn0UCuxnlR/8APNPyo8qP/nmv5U+igd2M8mL/AJ5r+VHkx4x5a/lT6KAuyPyYv+ea/kKXyov+eSfkKfRQK7GeTF/zyT8hR5Uf/PNfyFPooC7GeVH/AM81/IUeVH/zzT8hT6KAuxnlR/8APNPyFL5Uf/PNP++RTqKB3Yzyo/8Anmn/AHyKPJi/55p/3yKfRQK7GeTF/wA80/75FHlRf880/wC+RT6KB3Yzyo/+eaf98ijyo/8Anmn/AHyKfRQF2M8mI/8ALNP++RTXtreRCkkEboeqsoIqWigLsjEEIAAiQAdAFFL5MX/PNP8AvkU+igV2M8mP/nmv5UCKMf8ALNfyp9FA7sb5Uf8AzzX8qTyY/wDnmv5U+igLsb5Uf/PNfyo8qP8A55p+VOooC7G+VH/zzT8qPKj/AOeaflTqKAuxvlR/880/KgRRg5CAfQU6igLsUnNIehooPQ0CLdh/yDrb/rkn8hViq9h/yDrb/rkn8hVigAoFFAoA49/+Pq6/6+JP/QjXJeOPEceh6fJskkW9khxbYgZxuLbRkgEdT0PXFda//H1df9fEn/oRrivG8WpXeqaFYWV1BFHNOJGWWAvgxAuGPzD5chRj1PWuuDstCZpOOpFpHiWLVvE11Z28sjW0NtHtDW7pukOSxJKjHG3g+tXNat9YkUz6bq6WUccbFo2tFl3sMnOSRj0rM8JQ6vJJf6jd3ltJDd3cm5Ut2RmKYjDAljhcJ0569a6O7BayuB1JibA/A1vG7jqcUrKWhyui3PiK48Nx69dazDKj2Tz/AGYWarhtpK/MD2I9KtaB4y02+sNLjurz/TrqKMM/lMsbSlRlQ2Nu7OeM03REcfC+3QowcaY6lSOc7Dxis+aFl+G3hyNYm3JJYNtC8g71JOO3U1F2rF2i2zo9Q8TaVpd2bW5uH85VDyLFC8nlKe77Qdo+tacM0dzCk8EiywyKHR0OQwPQg1wLTHRvEOvjUNcudK+0XRuYttssi3EZUAbSVOSMEbf0rp/CVotj4XsoES6RMM6pdqFkUMxIBC8DrwOwqozbepnKCS0FvvFOj6dcy289y5eHHnmKB5Fhz/fKgheOealvPEWk2C2zT3ij7TGZINgLmVePu7Qcn5hwK5KfVZpJtdt5L99NlE8iR6bZ2QM1zxgSFiDuL8cjoO9TeH4ma78FOYifL0iYZK/dYBBj2PWp53cr2cbHSweJdJn0qfUlvFW1t2KTtIrIYm/uspAIPI4x3qOLxZo8sd0/2l4zax+dKk0Lxusecb9rAEr7jNc9qFvA114yN7a3j2zTWjlrUYkXCA+YvqVOCevSn6RcSalrMunnVF13TpbKRJLx7UJJADgBC4ADbsnjrxmnzyD2cTrJdVsYL2zs5blVuL0MbdOTvAGTjt0qOfXtMtYryWe7VI7OQRTMQflcgEKOOTgjgZrz2CG+fTpNVuLaU3Phv7PbRrt5kETnzSvqGQj8quXdhdW/h/QtUmaeHdqL6hfPFF5jwmUNh9pB+6CoPBx+FL2kg9nE7Ky8Q6ZfRXLxTsptU8yeOWJo3RcE7irAHGB1xS6V4h0vW5po9OufPMSqzMI2C4PTBIwfwrlYzDqF7qd5banf6q8GkzRNcmBEhG4ZCZUAs3Ge4FdR4Yi8nwpo8e3btsovlxjB2DP61UZSbFKEUhzahHBq97HPqESwW9qkzwtHjyhlsuX7g46dsUaX4j0rU9QjtbadxOw8yNJYXjMi/wB5dwG4fSsLVYEm8ReIVuLOe6t30eFWihHzuNz5C+/eq3h/UpG8R6TbWOsNrlmwbeLm1AmslC8EyYHJOFwRmpcpbFRgj0/vS0gGM0tZ2O8KKKKLAFFFFABRRRQAUUUUWAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKa/wBxvoadTX+430NIR1Omf8gmz/64J/6CKKTTP+QTZ/8AXBP/AEEUVzAUYfv3H/XeT+dS1FD9+4/67yfzqhr3iC08N6aL27EsheRYYLeFd0k8rfdRB3JpCNSiuNuPGGtaVB9u1zwjPZaYMGW4hvEneBT/ABOijOB3wTiuwikSaFJonV4nUMjqchgRkEfUUAOornvEWua3ovmz2WgJqFlBbmaWc3yRFcAkgKQScAfrWZaeMNbn8Pz67c+GktbBdOe+hk/tBZDJhNyqVC5GR37UAdpRVXTrv7fplneFQjXFvHOYw2doZQf61ayAQCRk9B3NABRWd4g1Q6H4d1HVRCJjZ2zziMtt37RnGe1W7Wf7TaQzEBWkiWQqDnbkA/1oAmopCQCBkZPQdzWTBrbTeLrzQ/IAFvZxXQm3ctvZlxjHbb1z3oA16KTK4J3AAdST0pc0AFFJkYJyPl689KXqARyDQAUUhIDBSRuPIHc/hVMapbnXW0gB/tK2ouycDbsLlBznrkGgC7RVHVtVt9Gs47q5DlJLiK3URgE75HCL39Tz7VWu9Za08V6Zof2cFb23nmMu7BTytvGMc53fpQBr0UAhsFTkHoR3oyD0IODg4NABRXN+IPEt5pOuaVpNhpS391qKTOoa5EKoIwCckg/3qk0XxPLf6zc6JqWky6ZqkEAufLaZZkkiJ27ldeuDwQQKAOgooyCxUEEjqB1o/l60AFFJuAAJIAPckYpQMnFABRWPfa4bPxHo2krCrrqSXD+dv+55ag9O+c/hWv3oAWisfxTry+GfDl1rDW5uFt9mY1baTucL1/Greq339maPeaiYzILW3ecoDjdtUnGfwoAu0ViWWs32oJoNxb6SWstSt/PuJ/PUfZcoGUber5JxxWSPHLNoF1qYsFzBrX9leX5p+Yease/OP9rOP1oA7GilOAcbhk9ATyayPFGuL4a8M3+stbm4FogcxBtu7LAYzjjrQBrUVyMvi/U9Nu7Fde8OvY2d7OlvHdw3iTqkj/dDgAEA+vNdcSFxkgZ4GT1oAKKyZtbMPi6y0P7OCtzZy3RmLcrsZV24x33dc9q1uOcEcdfagAopAQVDBgQehBo3DJG4ZHUZoAWiisd9bZPGKaCYAFbT2vTMW6YkCbcfjnOaANiilAyMgg/SqWqX407RL7UUQTC2t5Jgobhiik4z+FAFyiqek3x1LRrHUGQRG5t45imc7dyhsZ79aucbtuRuxnGecUAFFGQCASMnoM9fpWR4Y1s+IvDdlqzW4gNyrHyg27bhivXHPSgDXooooAKD0NFB6GgC3Yf8g62/65J/IVYqvYf8g62/65J/IVYoAKBRQKAOPf8A4+rr/r4k/wDQjUE1qZ2Vt2MDHSp3/wCPu6/6+JP/AEI1R1bU5NLtI5IbC5vp5ZViiggHJY92J4VRjkniujmajdDUFLRjv7Pbp5gx9KPsLD+MflVLSdflvdUudK1DTJdO1CCEXAjaVZVkjJIDKy+4wRWMnj+RtDg10+HrxdIJ/fXJmTMY3bSwTqyg9+KXt5j+rUjpvsLf89P0pfsLD/loKp6vrz6ff2mnWOnyahqF2jyxwrIsShFxlizdOoxjNVx4oaPTPtd5pd1ZyR3sdlPDKR+7LsFDhujrlhyKPbzD6tSNP7C3/PTj6Uv2A/8APT9KyZfGNtH/AGqsdvJLJp97FZFAwHmvIyqCDjoGJH/ATT7zxJdDU7yy0fQ7nVDY4F26SrGqMRnYu777Y5wOmRR7aYfVqJpiwbrvH5UfYW7v+lZ114rsofBreJ4Y5J7RYhL5fCt94KQfQg5yPap9J1fUNTuHM2hXNjZFN8NxcSpuk5GAYxyvHPNHtpj+rUi19hb++Pyo+wMRjeMZ6Yq7WTqviG00eeOG4gv5Gdd4NtZyTADPcqCAaPbTB4WkWvsB/wCen6Un2Fuzj8qbpWr2+sQSS28V3GqNtIubd4WJxngMASPer9CrTBYakU/7PcgHcMD0FJ9hbP8ArBXL+MNJsLa/0jVoYGS/l1i1jklWRhuUnBGM47DtXRa7rceiW8LfZ5bq6uZhBbW0ON0shBOMngAAEknoBR7eYvq1LqTfYm6eZ+lKlmUcMH75PFUNK1973VZtJ1DTZdO1GOITrE8iyLLGTjcjL1weCO2az5/GFxaQNqFx4dvodGWXymvJJFV1G7bvMR+YLk9euOcUe2kH1ekjqsUYrO/tiOPxJHo0kDI01qbiGbd8sm1sMoHqMg/Q1WHiazEGt3cytFY6TKYpLgnIkZVBcKB6Ehfc1HOzblikbWKTFc9beJ7oXtjDqmh3Wmw6i/l2k0sqOGfG4K4U/IxAOBz0xTr3xLdLq15pmk6JNqctiiNdETpCE3DcAu7ljj6D3o52FonQYo71W029XUtOt71IpolmTd5c6FHT2YHoetWj91vpT52PliGMGkxXnHhXxZd6T4F026u9Fu5NMiBSe/MqkrmQjfs+8UGQM12Wqavd2d3b2mn6PcajczIZPlcRxRoO7SNxk54HJPNLnYJRNXGTijHFcT4g10az8OPEki281ld2aSW9xBIwLRSLg8MOCMEEEVu2mvJc6umlWcD3H2eJTeXCsBHbkjhCf4nP90dO9P2jJtG5sYpQK5+68S3P9o3dlo+h3GqNZYF26SpEsbEZ2KW++2OcD160XPi6yXw/Y6taQXF42oOsVpaoAskkhz8pzwuMHJPTBpc7HaJ0GD6UlcXpl/fXnxLdL7T5rCRNH5haZZFb99wysvB9Oma7StIO6IlYKKKKsgKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKa/wBxvoadTX+430NIR1Gmf8gmz/64J/6CKKNM/wCQTZ/9cE/9BFFcwFGH79x/13k/nXIeOnWw1jwnrV0dum2GosLqQjKxeZGUR29AG79s118P37j/AK7yfzp00UdxC8M0aSxSDa6OoKsPQg9aQjG8Vavp2neEtRuruaJ4ZLZ0jQMCZ2ZSFVR/ESSBxVDwvoMVlpfhY6jNImrWOm+SLfz9oIKruyn8W3gZ7VHe/DTwzLbudO0+LTL9WElveWwIeCRTlWUE4xnqOhret9LR5LC/1OO3udXtYmjF3HGUxu+9tGTjPpQAzxL/AMirrP8A14z/APoBrm48f8KNBI/5l3/2hXazQxXEEkE0YkikUo6N0ZSMEH8KhGnWY0v+zfs0f2HyfI8jHy+Xjbtx6Y4oA86m0aw0TTPA2rafD5F/LeWcM1wHJeaOVDvVyfvA+nbAxUFlotz4oXxHc3drpk1+l/cQC6urqRJrFVOI9gCnywFwwIPOcmvS5NMsZre0gltIXitHSS3QrxEyDClfQgdKoaj4T8P6vffbdQ0i1uLkgBpGXBcDoGxw3/As0AZvipbhPhRq6Xc6T3K6Q4lmT7sjCPlh7E81gatpOn6ND4O1TRowur3F/aw+cspL3UTr+9DnPzDHPPTFehT2Vrc2L2M0Eb2kkZieEj5SmMbcemKo2PhfQdN1A39lpNrBdkECRF5UHrt7LnvjGaAPOfGf9nXmheIPEGlaK/n2kzbNbuLvZIk8bhSIVJLbQQRt+UH0qz4mvdZtfGWsXGmAhG0eyN7PDzPDAZH3vEmMFgM/TqM13Eng/wAOTXN1cS6NaPJdbvO3KSHLDBJXpk9yBmtNLC0jvnvY7eNbp4lhaUD5iikkL9Bk/nQB53qFlaXniTw5ounRWV3oLaXJdWkF1O4hupNygsxAPmMFOcH+8TUWu6Pe6R4PvLU3UMEMut2bW0NjOz/Yw0kYZVZgCOcsBjAzXdTeFdAuNMTTZtJtms45DLHDtwI3JJJXHKnk9MU+Dw3oltpw0+HS7aO0Eon8oJwZAQQ57kggcn0oA57XfA0Eel29l4etLRUW+F1cWN1M4jv8KQVkbkk9DzkEjmtLwVd2c+mXdtaaR/ZMlnePBc2YcOkcoAJ2MOCuCOmPoK1dV0bTdct1g1OziuY0begfIKN6qRyDz2NSafptlpNmtpp9rFbW6ksI4lwMnqfcnuTQB5X4ok0+50K98T6LozRSR3ytFrU13iVpFmCMI1OTs4K7flGO1b40XTJ/jRqDy2UbMNJgulJJ4k85hu69eBXSHwd4baaeVtFtC87F5MplSxOSwXOASepAzVm/8P6TqmoW2oX2nwz3dt/qZXHzLznHHUZ5we9AHNfE7T7W90jSnubdJGXVrRATnhXlAYfiKh1vQtMl8f8AhfS2tgLBLG+/0dWYKeYzg88jPUHrXZ6jptlrFjLZalax3VrLjfHIMg4OQfrnvUNroemWTWjW1jDG1nG0duQOYlYgsB9SBmgDzLVTL4f0/wAbaZpEjWenQ39gAqOVW2imVfOKkZKL9OmSRXVaT4Zm0rxJYXllBpOmWvlSJcW9lPIxu1IG0kMoBKkZ3cnBNdQNLsPNvJDZws18At1lQfOAG0BvXjiqel+FdB0W5NzpulW1vOQV8xVyVHouc7R7DAoA5nxbb3F38SvCEVrfyWMpt74ieONXI+VOMMCKqeZfaD4w1q3W6XWtVn0SW5ju2QCa38vOyIqvyhSxyMAEnOc12uq+HdH114X1XToLtoAwiMq5KZ64+uBTtL0HStDWRdK022sxKQZDCgBfHTJ6mgDz/wAP+Hri50HQNZ086VBeM0Fy+rG7kae4JIMiyZX5t2WUqTgE8dKL+7udC/tvwTaOyXOpXaf2S2eVhuSfMI9oysh9siu2i8H+HLfUhqMOi2iXYfzFcJwH67gv3Q2e4Ga0ZdNsZ9Rt9QltInvLZWSGZl+aNW+8AfegDz1dFF/411XSZNP0++tNKtLWGwtNQndVjhKcyIoUgksMF+owKjvNFnN14H0TU783UbXN7HI9vcMQ8IQlYi/BYAAKT1ODXeat4d0bXHifU9OguZIhhJGBDKO4DDBx7dKlj0XS4BZCHT7eMWOfsqogUQ7hhtoHTIoA5a70yx0nx14N0+xtY4LNIdQCQqPlGUUkfjk1ghrmGKb4ZiSVZnvgkUuTu/sxsysc+wDRfiK9OksbWa9gvJYEe5tgwhlI+ZAww2PTIrNtdEkHi6+167kidnt47SzRV5iiBLPk+rMfyAoAxfinGqfDHVYogEULCqgfwjzUx+VM8SaJrcfhPV3fxZdyItjMWjazgAcBDwSFyM+1ddfafaanZvZ31ulxbSY3xSDKtg5GfxAqSeCK5tpLeeNZIZUKOjDIZSMEH8KAPP8AS2P9o/DFQxAOlzZA7/6PHisUf8iJqf8A2OZ/9Kkr1OPSbCN7F0tIQ1jGY7Uhf9SpG0hfQYAH4VH/AGFpRtXtjp8HkyXH2p0C8NNu3b/97IzmgDiPFWmf2TrereJdV0m213SP3bM/2grc6cqgAiMHgjPzcFSc961/igwb4Xa6wzhrdSOP9ta2bvwpoF9qf9pXOlW0t2WDtIy8MwxgsvRiMDBIPSr99YWup2MtlfQJcW0w2yROMqwznkfhQB594qtb3RYtF1XUNbbWYoL6AR6bcQpH5jsQqsmzGXTOQGBHB470/wAXwaVrL+I54tD+3XGnQmOfULi78pLWRY9yiIEkhhlSSoGT3NdXY+D/AA5pl4l5ZaJZQ3Mf3JRHlk91Jzj8KluPC+hXepSajcaVby3cgAeRgfnwMDcOjEepGaAPPb6/197vwxfaPHHdatJ4WlkJmPzEnyizKOjP3AOAT3qW+WzuLLwfY6TOt1pGqXM0t097O6fbLgJkLOygncW3ZU8ZUDoK9Ih0qwt5raWK0iSS1g+zwMq8xxcfKPb5R+VV5fDuizWl3ay6XaPb3c3nzxGP5Xk/v47Nx1HNAHC67ot5oXhDxi6mxtLOTT1eOwspXZYJBnLgEDYGGOAMZXNauq+BraLwrdTaKjJrpWK8S7dy0k88XzrvJPOcsPT5q6G18K6FZ6fc2FtpdslrdDbcIVJ84ejEnLfia05VlS3dbYIJVQiLfnaGxxnHagDi/DOqJ418Sp4ggDDTtOtFggQ8D7TKoabPqUXan1LUmr6bY6t8WbO21GJZ4Tobt5Lsdsh88cMM/MO+Dx37V0fhjRF8PeHrbTi6yTLmSeVVwJZWJZ2x7k/lin6n4e0fWZhLqOnQXEoj8tZHX5lXOcBgQRz6UAcFYz6Xptp4n0aWC4udEGsw2Wn2ttOQWldFZoFbcNqB+ozgAke1V1hOlav4v02HTYdKtX8NtcvYwTiRFkG9d2AAFJU8gdcA16KfDeiNoo0Y6XbDTQci2CYUHOcjHIOec9abb+GdEs45Fg0u2QSwm3lIXl4yclWJ5bPvQBw/g2W4u7/TINdTZdW2kwy6JbK2YJV8oB5AeN0oOAQfug8ZyTVDQtDvtc8HW2sR/wBlxaxK/nSavNdSi4imEnKt8uABgrszjHFentpGnulkjWURWxKtajH+pIGBtPbjj6VSm8IeHZ9TbUpdGs3umcSM5j4Lj+Ir90t7kZoA5uw0/TfEHirxZc6/HFcT6fdLBAkz8WsHlKwdOfl3Ek7uvHWtH4Y7f+Fa6HtOR5T4Pr+8eti98M6HqWpx6leaVbT3sa7VmdckgdAezAe+avWdlbafaJa2cCQW8edkaDAXJyf1JoAnooooAKD0NFIehoAuWH/IOtv+uSfyFWKr2H/IPtv+uS/yFWKACgUUCgDj3/4+rr/r4k/9CNc74xtdRu9Os1soJ7qBLpXvbW3n8qSeHByobI74JGRkCuif/j6uv+viT/0I0ord/CaQ1OI8N6Tc2/jK51GPw/JpOny6cII1klVmLiTPzAMdpx254FNXQdUHwak0P7I39pG0eMQbhncZCQM5x0PrXdUE8YrE25TlvEtjJcyWAuPD/wDaljHGdzW0m26t5ezIdy/KR1wc1TtdH1m58G63Y3a3Q812bTbe8nEs8SgAoruCf4xxycDvXaijFMOU4Cz8L6mmpeHZ5oMK4+0aqdw+WZWeVfr88jD8KhuvC0dp4i1e5vPDd5rFvfzm5gntLna0bEDdG6714yMhuetejZ9azJfEOiw6kNNk1WzS9LhPs7TAPuPQbeuTkUC5TC1XQZ2+GM+j6fpS2lw8S7bKObzAjGQMRvPXvzXXjoKKKCkgrM1LSJdQmjkj1nU7EKu0paSqqtznJBU81p0UBYpaZp0mnQyJJqN7fF23b7t1Zl46DAHFXsUgpaAON8XyatfT2NrZeHr+4Wz1GC6adXhCOqckLlwc845AqfWrfUtSXRNdtdKnS8026eV7CeRFkeNlKNggld2MEc11RFL1Az2oFa5y9hBqGq+Lhrlxp8unWtrZPa28dyVMsjuwZmIUkBRtAHOTXGXvh3Wbzw1c2Nz4ZvLvX2bdLqc10pSTEmcx5b+7wFwAK9a70UC5TnvFVjfzWNnqWkW4l1XTZhPbxMwXzARtdM9sqT+IFVbrwi83w4l8OJMBdSQ7nmbo85bexY+hbP4GusqK4uIbS3kuLmVIoY1LPI5wqgdST2oHY4XR9AtTqOns3gq6s7mBxJLcT3u6KJlH3k+dt5z04HBq14g095tZuZbnw1dXTbALPUNJuRDMBjlZDvXGD0PIx2rp9O1nTNXSR9N1C2u1jIDmCQPtJ6ZxV3OBnsBQFlY56x1OTw/4X05vFV8FvWXZLJtL5bkgEqOTjGT0JzVvS/Euj6zcvbafeefMqFyvlOvA75YAdxWha3lve20dzZ3Ec0EgyksTblYdOCKn3MepJ/GgLaHmdrZeIz8P08It4fniuZo2ga7aaMwxxs5JY4bdkA/dA61s+I9NvW1vT5JNNu9X0WC1MRs7WYIRNkYd1LKHG0Y68V2eabQHKecweHdVHgvxhZDR/sk2oTNJaWqSq42lEAG7OM8HNbGjaFd+EtVhh0yGW40S9UG4iaTc9rOFAMgJOSrY+YcnPNdeKD1oFyI86u/Csdn4i1e4vPDl3rFvfTm5t57O52NGxADRuu9cDIyG54Nad3od3Z6L4fuNK0eOGbSrn7Q2mJPu+V1ZXVXbgsN2fTOa7LHFAHagOU5LTY9Uv/HT6xcaRcWFl/Zn2dPtDoXL+Zu5Ck4/+tXV0kc0VwnmQyJImSNyMCMjg8/WlxWkJJEygwooxRV8yZLi0FFFFUQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU1/uN9DTqa/wBxvoaQjqNM/wCQTZ/9cE/9BFFGmf8AIJs/+uCf+giiuYCjD9+4/wCu8n86zvEFxrMNrbxaHbQyXVxMImmnBMdsmCWkYAgtjGAB1JFaMP37j/rvJ/OsXxb4ss/COkrd3K+ZPM/lW0G7HmP7n+FR1Ldh70hGbb+I9R0PXNR0zxDc2t3DbaYdTS6t4DEwRWKsjruIzxwQeaoy+IPFth4Zi8W3n9nvYFVuJ9LjgYSRW7Y5Eu7l1Ugn5cHkVU0ttG8SaJ4g0+21y31PxNq9lJ9pmRHVV+XaqIGAxGpIAHU5JNR6h4ptdV+HX/COWwdvEd3ZrpzaYYyJYpcBHLjHCAZO7pigDoZdW1rXfEmoaZoF1aWVppscfn3dxbmYyyyLuCKu5QFCkEnrzWh4V1u51rT7pL+GOHUdPunsrtIiShkXHzLnnaykEZ9a57TdQsvBHifW7TXLlbS0vjDdWl3KCI5dsSxum7GAwKg46kGtLwLFNNHrWtSQyQxatqT3NskilWMIVURip5G7aT9CKAJ/D3iB59O1y91e6higsdUubcSvhFSJGAXJ/HrWlpPiLR9daVNL1GC6ki5dEOGUdiQcHHv0rzieGQaHeXU1vJLp9n4ylub6NUL5gV+WK9WUHax4PSt2fVbLX/iHo2oaBKt8mn2V019cWoyhRlHlxbuhYsMhe2M0AdLa+K9AvtVGmWur2k16SQIkfO4jqAehI7gHNXYNUsLnTm1GG8hezUOWnDfKAuQ2T2xg5+leXXGuya/a+FriO405Sur20p02xtmL2QLEESPn5TzgjaMk1b1eyurbxDf+CYI5BYeIbuO9WRc4igOTdLntkoAP+utAHc3nivQNPitpLvVraFbmMSw7mOXQ/wAeAMheep4qW+8RaNpljb3t5qVvFbXOPIk3bvNyM/IBktxzxXEyzR6D4/1+TUdbOixXiQGymeCMxSwqm0xqzKcFWz8uec5waRrDQtL0rQLmy8VNpk9uLhrC8vrcCORJHy6MjBQBnGAMHHTIoA9Dsry21G1iurKdJ7eUZSWM5Vh0rNTxb4efU/7NTWbM3hfyhEJOr/3M9N3tnNVdC1DU9f8AAxupIo7XUbiCZIzGCiM2WVJFDcgNwwB9a4SXUdLn+EFv4Vt1H/CQCCK2XTVQrPHdBhlyuMjDAtv6Y70AdVe+LfN8fL4ft9YsbKC3ijebzE3yXErOV8lckBcAcnk8ir/iPxA+ieIvD0ElzBBp929wLppcAbUi3L8x6c/nVfT0cfFLVfM+Zho1orNjq3mSZqv4zubS08X+Dru/2i0hnumkkZNyx/uhtY8cAHHPagDoLTxPod/pt1qVtqltJZ2uftEobAix/eB5H40tt4k0S8nvobfVbWR7Bd13tkGIRz95ug6HvxivOtdni1oePNZ0pvO0x9CS1NxGvyXE6liSp/i2qQM+9dD4w0pj8Mo7TTrQ+VCLWSWC3jBZoUdWcBf4uMnHOffNAHS6X4j0bW5ZItM1K3upIxuZIyc7f7wBAyvuOKitfFnh++1X+zLXV7Sa9ywESPncR1APQkdwDmuesLvR9X8RWGoW3iaXV7ywgmaGC3t0XCsvzK5VRt6DCsRz2rnJ9ek1618LTpc6cpXWLaU6ZZWzF7IFiCJHz8p5wRtGSaAO+1rxZouktc2c2q20OoLAzpEzZKnaSu7sMnGM4zU/hXULjVvCOj6jdMGubqzillYDALMoJOB71xWl6rp2k2Pi/SNWbbrNxfXcnkNGWku43H7ooMZdduBxwMHpWvpNpfXfwXtbKx3Ley6EI4RnaQ5iwPpQBu2vi3w9e6iNPttZs5LssUWNZPvsOoU9GIx0BNJf+LPD+lvNHfaxawSQvslRn+ZDgNyBzjBBz05rgtR1TSdY8AaL4f0bB1tJLNIbJYys1pLGy72cYym0K+WOM/jXTeGoI28Z+OJHhQtJeQoWKjLJ9nXj3HWgDrYZori3juIJUlhkUOkiNlWBGQQe4rjPh34xu/E9jNDq0UcOpRfvkCDas1uzMFkA9ipU+496t/DUMPhzoquCNsLjB6gb2wPyrmNE0nUz8P8Aw3rmhQq+uaakqLDIdi3ELyMHjYn3wwz3X3oA3bzxhdt8StN0CwjjbTwzw3s7Lk+d5RkEan1AAJ/3hW5eeLfD2nagbC71i0guQQGjeT7hPTcei598VzEWh/2BrHgWzaQz3BvLqe8uMf62Z4GLufqx/IAVn6Tq2kaH4R13Q/EBQaxJcXZuLV4z5t6ZGYoyDH7zcpUDHT2xQB3+oa/pOkyeXqGo29tIY/NCSPglM7cgdTzxxS2+vaRc6O2rw6jbNpygl7neAi465J6Y9DXDeHLC7tPGvhSDUoyby28LFZC4yUcSIMZ9QCRWZq8HlJ4jla3Z9OtvFdtdXkaIWBhEcZdto+8M4Y/SgDrbfxha6r4z0mw0fUILmyuLW5lnVV+YOhTZ1AIHzH61qweLPD1zqo0uDWLSS9ZiiwpJncw6qD0Le2c1zx1fTtY+J2hXulyLeQx6bdq1xChKMf3ZCh8YJHPHbNc3feIH1/RdFljuNPgI1i1l/sq0tWMtp+/AJkfPytzz8oznHNAHqtrqVleWT3ltdRSW0ZcPKrfKpQkMD6Ywc0h1WwXSRqrXcQ08xCb7ST8mw9Gz6V534gsb2y8UX3hSxjkFh4skW48xMYt8cXf/AH0gUj3Y103xAhWP4Z67BBHtRbFkRFHQDAAFAF8eL/DrG6A1qz/0UAzfvPugnAI9eeOM81btdc0q90ltVttRtpLBAxe4DgIm3ruJ6Y75rmNVtIW8eeBj9mTbFBdhcKMJiJcD2rnNYs5XXxZstnlsrfxJaXd5bxoW8yAJGZPlA+bsSO+KAPRdK8SaLrkrxaXqdvdSIu5kRvmCno2Dgke44qODxXoFzfQWUGr2stzOjSRxI+SyrnJ9vunr6GuduNV0/wAS/EHwxPoE8d4bHz5Ly5tySkcLJtWNmxjJbBC9RtJqT4b2cMHw9jdbdUllkunkO3DM3mOMnv0A/CgDbj8Y+G5bu2tY9asnmugvkqsmd+4ZUZ6ZOeB1oTU2ttZ1o3uq2P8AZ9nBFKYQpWS1BUlmkb0OMj2FcHJbLH+z5peyDbIiWko2Jgh/PXLcd+TzVvxEjG++KGI2+bSIAvHX9zJ0oA7W18U6DeaoumW2rWk16wJWJJMlsdcHoSO4HIpl94u8O6bfPZ3us2kFxH99ZHwEPox6KeRwSDXNavAsPh/4feVCEMWoWONiY2Axnd9M96wby7tV/wCEo0K8vxY6JcanJJPNJp8sr4O1nKyoPLxkEAnlR+FAHrNvPFdW8c8EiyQyKGR1OVYHoQa4rxje+LtCt31G01fTDayXcUEdvJYEsgkcKMtv5xnPQZrqLPXdLvdTk020uhLcxW8dyVVSR5T/AHGDYwc+xrB+JQJ8KQ4BP/Eyszx/12WgCtrupeI/DGl2lxqGr2Ny8+q2tuZI7PyVSJ2IfOWbt34xium0vxDo2tyzw6XqVvdSQYLpG2WAPQ89R79K5/4mQ+dpWjxtH5itrtmHUjII3859qi8SWs8/xBt0sQI7qfw/ewpIBj5tybBn2PSgDoLfxRoV3qY0631a1lvCxQRK+dzDqoPQkY6A5q7banY3li99b3cUlom/fMrfKuzIbJ7Ywc15loTaNfeG9D0O78R3Fvc2r24/stbRFnhnjIOCAm4DcDl+4JOeasa9ZXlt4nvvCdnGy2HiuVbrzI+luB/x9j23KFx7uaAPRRqVi2lrqf2uEWDRCYXDNhNhGd2T2xVfTfEOj6xFPJp+owTpAMykErsHPJBxgcHnpxVTxVYaTceEbuy1KZ7PTdiKZYgT5OGG08A8AgdRj1rhtR1HWPEPhXxLpVve2+vRW8EDpqNhDsNwu/MkBAO1nCKfun+LGOaAO/0zxVoGs3ZtdO1a1uZ9pYRo3LKOpXP3h7jNUPD/AIhe4tdfutWuoIrbT9UuLcSvhFjiTbjcfxPNYer6zpPiXVPCdv4cnjubm21BLg/Z0I+y26od4fj5Mghdpxk9qxb2Jl0LV55YJJLC28ZG4vUCFs26spYlR95QdpI9qAOut/GNrqvjLSbDR9QgubK4tLmWcKvzB0KbOoBA+Y+xrrcfLXDDVtO1v4oaFd6XMl1AmnXatcxKTGSTGQofGCRzwOma7k9D9DQMuWH/ACDrb/rkv8hViq9h/wAg+2/65L/IVYoEFAooFAHHv/x9XX/XxJ/6EaUUj/8AH1df9fEn/oRpRW7+E0huLRRRWR0GZqniHStEeNNSuhA0gJQeW7bgOv3Qasadqllq9oLqwm86HcV3bGXkexANW8kZwSM0uc0AJiuV8bRxiHQ3CKG/tu0y2OT8xrqx1rlPEuj+I9YuLdLOTSI7S1u4ruEzeZ5jMnOGxxgnPTtQSy/4j1m605tPstOihk1HUp/IgM+fLjAUszsByQAOg61X0zVdUt/Eh0LWTbTSSWxuba6to2jDgMFdWQk4IyDkHoaNS0XVdUstOu3nsrfW9OuDPC8as0DZBUoQfmwynk9jT9N0bUZdffW9amthcLbG0toLPdsiQkMzFm5LEgdgABQJ3Oe1PxnqekCW7u7/AELMMwWTSI2L3AQtt/1gbG/HONuO1dRcapc2njCy06ZY/sN/bv8AZ3wQwnTllJzjBQ5H0Ncq/gnxEvhWXwzBe6SljklLkwv58o37wH7A+rDJrqfEuj3Gs6RGlncLbajbypPaznkRyLxz7EFh+NAK5mjxc8Oia/r1xErabZTvFZqgw02zCEk88GTIBxwBWbY+OJ11fS4brV9B1BNQlEDQacx8y1dhlTyx3rkYJwOorop/C1nP4N/4RosyW32dYRIv3gRg7/ruGah02w8RR3Nqt+2iiCA5kmtrdvNnwOODwnqSM+1AO5n6r4k1EeJrrSLa/wBJ0vyIkeBtRRj9sLDJ2HcoAHQ4yc11Ony3c+m20t9bJbXbIDLEkm9Vb2Pcd6xNX0vXLi5u0hbStQ0+5UYttTiJFucYO3aDuB64POe9Safa3PhLwxp2nQ2t3rEkCeWxgKKR1OfnYfKOgGSelAK9zoAATzXB2vinXdTubg2c+jpcwXLRf2JcZjuWRWxneWA3EfMMKRXUabqt7fXLRXOg31ggXPmXDxFW9vkYnP8AhXOar4X1/V7J9MvbnSrmEy5j1KWFhdxJuyNoA27x0DAj6UA/I1dT1bVJ/EUehaN9mgnW1+13N1dRtIsaFtqqFBGWJB6nAAqXw9q91qMmpadqkMK3+nTCKbyQfLlVl3I6g9Mjtk4xUepaLqCa/DrmjTW/2oW32S4hu92yaMNuU7l5Vgc84Oc07SNG1DTrfVb2Se2m1rUZPOc7WECFV2ogHXaAOvU5oDUq+HkUeNvFqooVfMtOAMf8sab/AGtrusarq0WjNp0Nlpkn2djdRu7XEoUMygqRsAyBnBPtTNK0jxTZeIL/AFKeTRWj1CSJrhI/Nyqou35M9yPWpW0PXNN1fU5tEudP+yanIJpku0bdDLtCsybfvAgA4OOe9AtbC/Dg5+Hei5GP3Tf+htW9qdy9lpN7dxgF4LeSVQ3QlVJGfyrE0m1vfCPhLTdNSxn1WaBTG/2MouOSd3zsvHNT/a7/AFyyvtPl0S+03zraRFmuniZMsNoHyOT3z07UFLaxzA8T+LGj3CXSAX0YauubeT5QOsf3+c/3u3pW0fEGqave6fp2jLa2882npqF1cXKNIsKv91FUEZJOep6CkXwldBI1N1D8vh86T0P+s4+f/d/WhfDeqabcaZqGk3Fob630+PT7qK5DCKdFwQwK8qQc9jwaBal3Qdau706pY6lFCmpaZJ5crQ5EcoZdyOoPTI6jnGKzfCeq+KfEenabq850u1splzJCsbtLIASCQc4TJHA5+ta2haJcaeupXd9PFPqWpSeZO0SkRphdqIuecAdzyaf4W0eXQPC+n6VPKksttHsZ0zgnJPGfrQCTMlNT8S6pqmu2mmyabbx6dc+VHJcRPIZMxq20gMMcn73PUccVe0zU9U8R+E9N1LTHtbK8uQDILiMyouCQwABB6jg56VZ0fSJdO1HW7iSVGTULoToFzlQI1XB/75qnpOk6xoHhKw0ywlsJby3JDPcBxGQWZuNvOeR+tAJMTwNePeeE45nt7W3kE86MlpF5ceVkYEhcnGcZP1rCTxT4o/4QuPxU66X9ljXzJrQRPvkjD7WYPuwp7gYP17Vs+EdG13QrYWF7JpktmGll3QeZ5m93LY+bjHzEflUY8KXQ+GbeF/tEJuWt2h87B2ZLk59elAa2LGqatqc/iK10TRPssMrWpvLi5ukaRUjztVVUEZJOe/AFP8NarqWpTaxb6mlss1hefZgbYMFYbFbPPPO7Pt096z/EH/Eq8R6bqdrqmm2l+1m1rJDqLskU8QIbhx0YE5x3BNN8AvPcx69qEs0U63Wps0c8CFY5QEVSUB5K5BAPfGacdyW3Y7GigUV0GQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTX+430NOpr/AHG+hpCOo0z/AJBNn/1wT/0EUUaZ/wAgmz/64J/6CKK5gKMP37j/AK7yfzqUgHkgGoofv3H/AF3k/nXNeO9RuLLT9Nghvm0+C+1CO1ur5MA28TAkkE8KSQF3HpupCOp2gdhQANxbA3HjPfFcTplz/ZfxATRLHWbjUrW40+S5mhubn7Q1s6soQh+oDZPyk9sisrWYNT0WztpX8UXdx4xnnR4bGKf9xNlwGjWAjAjC5+Y8jGc0AemMgZcMoYZzg80ua5b4jXM9p4B1a4tppIJkRNskTlWXMijgjnvVzUvE0NjqY0q10+91K/EQnkgs0U+VGTgF2ZlUZwcDOTjpQBtjjpxQqhF2qAoznA4rDg8WWN1oI1e0t766UzG3NtDbkzrKCQUZP4SCOSSAPWqf/CeafHaXk17ZX9lLYzww3lvNGu+ASkBJDhiChz1BP0oA6gKozgAZOTx1pcc1m3+t2en6rZadMWM12ssgKgbY44xlnck8LyBn1NZlp42srtrOU6fqVvp17IsVrqE8IWGVm4XHO5Q3YsoByPWgDpGUOMOAwByARnmh1Egw4DDrhuawLfxZDe6hLb2elalc20NybWW+jiUwrKCAw5bcQCcEgY4pfFk9vDa6Z595f2wk1O3jjaxYKzuW4V8/wHvQBv8AJ60mBu3YG7pnvXN3PjKCPUdS0+00rU9Qu9OZRcpawghQyhwQzMAeD065B4rW0/V7XVNEg1aw8y5tp4fOjCL87j0AP8XbHrQBfAyc96CB3HNeax+LL/Wfhj4ivJIL63uYoL5orooI1UK7hFVlOdygAdOoPNb2leMIGOjWdzYalAl9GkVte3EQWKeTZnA53AnBI3AZoA6sABdoAC9MUvfmuZufGlnDLePDp+pXdlYO0d7fW0IaGBl+8OWDNt77QcVralrFppeg3Gsys0tnBAbgtCNxZMZyOeeKANBQEB2gDJycd6QKq5woGTk4HU1y7eO7NLuK1k0rVkluk32KNbgG8HGRGN3BAOTu28c9Kmh8a6Y+i3+pXMd1ZfYJvs9zbTx/vo5Tjam1SdxbcMYJzmgDo8DeGwNwGAe4oAwMDgVg6f4phu9Wh0u807UNLvLhGkto72NQJ1X720qxGRwSpwar6R42stcmjXT9O1OW3PmCW7MAEMLJnKs2eTx2z1HSgDptqhmYAbm4JxyaMAHOK5CD4iabNp9jqQ07VU0u7dIxfvABDG7kABvm3YycbgCM96mGrWVh4k8Tzvd6lK9hZQ3FzbOQYY02swMQ/vEKc574oA6kYHAGKPYVzNv43sp7nTlfT9St7TUmEdpezwhYpHIyq9dwJGcEgA9qZqPjyx09dQnGm6ldWGnyGK7vbeJWjidcblwWDHGeSAQPWgDqe/0pCAWDkZYdD3FVm1BU0s38kFwqiLzTD5RaUDGcbVzlvYVlaX4pi1DVv7LuNM1HTbxoTcQx3sSr50YOCVKsRkZGVOCM0AdB15703vxXIR/EXTZNPtdUGnaoNKmdY3v2gAiiYttG75skZ4JAIz3rTvPFUFrrr6Ta6df6ndQKsl0tnGrC3Vvu7izAZIGQoySO1AG4FCgAKAB0A7UoVRuIAG45OO5rkfhleT6h4Htrq5nmmke4ucvMSWwJnABzzwMDHasfxRb3mmadf6lqPiu9g1yeSQ6VZ2dxtjYg/uolhx+8J+UNkHk9qAO1GjW//CRNrTySSXAtRaxIxGyJN25io9WOMn2FaJGeK4bWr2+uPEnh/RNU1CXSLe6sXubh7ebyWnuV2gwh+wGS2Byas+DL6R9Y8Q6RHqcuqafp88Qt7qVxI6l0JeIv/HtI6nnnBoA7EA9abj5s4565ridL1xNN8QeO7vU7x1sLCe3Yb2LLEpgBIUe57DqTWtZeLoLjULOyvNL1PTJL0H7I17CqrMQMlQVY7WxztbBoA6BVVchVAycnA70uK5q+8ZW2nXTi60vVIrBLgW76i8AWBXJCjqd23JxuC4rSsNds7+XVIgXhk0yYw3SzDbtwoYN/ulTkGgDTwMYxx6UY5zjk9a5mLxtZ3dvp7WOn6jeXV/bm6htIYlEghzgSNuYKqntk5OelPbxpp39m291Fb3k1xcXTWcdikQFx56glkKkgKQASSTjHfmgDo+mCOoNcufBjR29zYWuvalbaVcvI8lnGIzgSEl1VypZQST3zzwRW7pt8+oWvnSWN1ZOGKtDcqA4I+hII9CDXM2XiLTNE0nVNQmutVuoF1mS1czgSNHIXC7IwP+WYJ4HX2oA6u1tYLG0htbaMRwwxrFGg/hVRgD8BU2ARgisLTfFEN9rR0m40+/0+7MJnhW8jCieMEAspBPIJGQcHnpVDxF4nu9J8WaFp0FhezxXPnvMIIlYyhY8hVJI5BIJ6dqAOtPTkd6TvnvXC/wBv3Fn8QdZgSy1S+DadaSxWduufLJ8zcTuYKp6Drz71sp400qTQrPVYUupReTG3gtEhP2h5gSGj2diNpzk4GM5oA6AKu8vgbjxnHNZy6NAPEkmuPJK9ybYWsSMRsiTdubaPVjjJ9hUeka/Dqlzc2b2l1YX9sqtNaXagOFbO1gVJVlOCMgnkGtagBRTVVUUKihVHIAGBTqSgBAoUsVABY5OB1pRxRRQAgUKAFAAHQDtQeh+hpaQ9D9KALlj/AMg+2/65L/IVYqvY/wDIPtv+uS/yFWKACgUUCgDkH/4+rv8A6+JP/QjQKH/4+rv/AK+JP/QjQK3fwmkNxaKKKyOgDRWfqepXVg8a2+i3uoBwSTbtGNnsd7L+lS6fdzXtr5s9hcWMm4jyZypb6/KSMfjQIt1iX3ie203VYbG50/UlE86W8dz9n/cM79Buz/nBrcArl/G+RbaDg9dbtP8A0I0A2dR+FJzXN+K728S80bR7K5ezfVbpopLlFBeONELsFzwGOMA9qh05rzRfGa6I+oXN9Y3dk11Ebxw8kLo4VhuwMqQw69MUCcjq/rRXlepazfW2kz69Y6vrl/NDPu89YAmnunmBSgQ8lQDjcMnPOa7PULq503xjpkjzv/ZuoxNaNGx+SKdcujD03AMv4CgOZHQ4ox7GuEfxFqFp4M1/xWHaUTSsdOiflEiBEaNj3OWPrkVX0691e11jSzbL4ouxNKI9QGp2oWEIRzImPuENjgcYoDmPQsUVw3iJ9Ri1+7bUrrXLPSFiT7FcaSm5EbHzmUKC2c9MjGK6nQpnudCs5pL+HUGaME3cKbVm/wBoDt7j1zQNM0O1FLkZGeleaT3OqWs80ut61q+kaiLpvJnaLzNNKbvkXgYwVwCWIOe9AN2PSqjuZxbWss/lSy+Wpby4l3O2Oyjua5rUnu9Y8ZjQY9QuLGyt7FbyZrRwkkzM5VQGIOFGCeOuRUvha8vRfa3ot5dteHTLhFiuZAA7xugdQ2OrDkZ78UC5i9oviC21yW8hitby2ns2RZoruLy2XcMjjPpWqTtBJ6AZrmdAH/FceLv9+0/9E1TtU1DxPrOuzHWb6wh065NnbQWpVVyqBi7gg78luh4wKBcx02kapba3pFtqVpv+z3Clk3rhsAkcj6irlcx8Oc/8K80Xn/li3/obV1A6/wCeKCulxaparqlpomlz6jeyFLeEAsQMkknAAHckkAV5xqWs31tpFxr1hrGt308M+4TrAE0918wKUCHkqAcbhk55zW78SdMF1okd19tvYsXNtH5MM22M5mUbiuPvDPB9hQS5HS6Tqc+ppI0uk31htxtF2qAuD6bWOPxxV6GeG5RngmSVVYoWRgQGBwRx3BrPh0MwWNxYjVtWk81iftElzmaPpwrY4HHpXM+ANFCW1xdjVNUfytTu0ED3GYnxIwyy45J6k+tA7ndYpMV5xeazeatr+sxPJ4jht7Cf7Lbro9uGUMFBLyN/EST93pj61oalq+tL8JZ9RuhNY6ykADkpsYMJAu7HbI5x70BzHb80VjaTo11YXD3d3rd/fzSx4eOVlWFW65RAPl6Y6niuIjl1n/hWcfiw+INQN/bxmdYiy+SyrIRsdcfNkZ5JzQFz0y4tre6iMVzBFPGTkpKgYfkaeqKqBEUKoGAoGABXK3815rfi6PRI7+5sLOHT1vZjauEllZ3KqoYgkKMHOOvFZkviLVND0TxVbNdfbbrSJYorW6nAyVmC7PMxwSu45Pfii4XR32KMVxl5BqHhW90aca3fagl5ex2d3DduGVy4OHjAA2YYdBxim2tnqGvaz4lgk17UbWC0vRHbpaSKmwmJTknBJHP3enX8HzMV12O0NJWJ4P1O51jwhpl9eOHuZIiJHAxuZWK5x74zW3W0djGW4UUUVQgooooAKKKKACiiigAooooAKKKKACiiigApr/cb6GnU1/uN9DSEdRpn/IJs/wDrgn/oIoo0z/kE2f8A1wT/ANBFFcwFGH79x/13k/nUWpiI6XciaxN9H5ZJtRGH870XB4OfepYfv3H/AF3k/nU1IRyPg7wzJpFheajcW1raaxqI3vFFGPKtVA/dwqBjIXqfUkn0rF12TXvE/h1tCu/CdxFrzlU+3/ILW3ZWB86OXdu7ZCgZ7V6Pjio4biG4UtbzRyqpKkxsGAI6g46GgDnvHunXmp+A9SsLKJ7m7kSMIi4y5DqT1+hNU5FvvDnjTVtWGl3mo2GqwW4DWSK8kMkSldrKSDtIIII6c5rscUCgDziOx8S6Vo9xew215A2q6095f29lskuba2ZcAJn5S/yruxnGTirOj+Gpr/WfE41Gy1CPTdV0+C3je/mEkr48wNnklSMg47cV39NoA898PeG9a1Ow1d/EimC/fTv7FtXJz+6VSGmH++xB/wCA03RNMQWumaXqPhHVmvLYxJNKbpjaqUIxKrGTBGQGC4z2xXotFAHnt7p90viYT+HtJ1rTNQkvle8kLqLG4i3fO7DcVJKjjADZxnvW7400+61G30YWVu83kaza3EgX+CNWJZj7AV0tA4oA5jw9pt5aeL/F15cQNHb3lzbvbSHGJAsIViPoeKk8Aafd6T4L0yyv7doLmIPvjbquZGI/QiujpqyIzOiurMuNwByV+vpQB5vZWGrL8OvEfhhtGvUvBHe+VKyr5U/mOzIEO7JJDDsMd639Z0y9ubLwgsNs7tZalazXCjGY0VCGJ+hxXVilIyKAPMrXQzo39paZqPhrV9SE93PLbT2Vw3kzxyMW2v8AvFCEZKnIwevNdR4o0uW4+HWp6Vp1mRK2ntBBaoQSDtwEB746V0dFAHL6hpt7N4j8G3Mds7Q2Szi4fjEe6EKM/U8Vhat4Z1e7m8SXFvaFpRrVnqVnE7hRdLFGm5Qe2cEc9xXo3WigDjnN/wCJ/FmgXX9j32nWWlSy3E0l8io7yMhRY0AY5HJJbpwOtWPA2mXml+BYbG8t3guQ1yTE2M/NK5Xp6giuoxS0AedSaBqx+CVjoospf7SSG3VrfjcCsysfyAJ/CrOp6JqM+tePJ4rSRotQ0mKC0Ix+9kEcgKj3yQPxrvO+aPagDjdU0jULjwx4RtorV3ns76wkuEGMxrGPnJ+lc00E923iO8Gl6le+GZr+WaSPT7yKOGdUwJCyPh/vI27DANjp6+sHORnODWBceC/DtzcTTy6XGTO5kmjEjrHI3q0YO1j65HNABqmo3994Jk1Lwyhe7uLZJrRXUBtrYPCtxu2k4B4ziuf0vTr648aaVqottc+yRWVzFJNq0g3732EAID8g4PQAH8K7xVVFCooVVGAAMACnUAecjQNW/wCFGjQxYynUvI2fZuN2fO3Y646c1p251Lw54v12b+xL3ULXVnhuLee0CkI6xhGjkLMNo4BB6cmuzo5oA5f4eaZfaR4NgstStzb3aXFwzx7s8NMzAg9wQc1k6zea1qOi6hoWpeEZr/UZVkit7mBE+yMGzsk3s2YyBgkdcjiu+9+9H1zQBy+rWN3beCLSybS4de1KGGKGNLiNXjMwUKZX3dFHJJ69u9X/AAv4eg8MaHFYRMJJiTJczhQvnSt95sDoOwHYACtrtSd6APN9T8J6rqh8cRxQCNry7s7mxMpAScwohIz2BK4ya0r19R8W6zoCf2Ff6bb6ffrfXc16EXBRSBHHhjvyW5I4wK7ajtQB5Vrlj4m17QNTtLyx1qXVvtHEUciR2SxLKCuzBHmfIBwcnPpWv4x8Oaxd68To8X+i67bpYarIpwYURwRJ9TGZE/EV3tN8+HzhAZY/OKlxGWG4rnBOOuMkc0AcPrehfYPFqasNHvr/AEuTT47Ix6dKyy2zRsSp2qy7lIbHHTHSlutK0xvDyCbwfq4ilvDcGOCbddwyAYWbIk3AkADAJPqK7vBJ4pkciSruR1cZIJVgeR9KAOf8Fx6tFo0y6s94w+0yfYxesrXC2/GwSkfxdffGM1zUmgasfD1/bixkMsnisXyJxloPPVt/0wCa9IpBQBzuoafdzfELRNQjhZrS3sruOWUdEZzHtB+uD+VVfFUV7b+I/Des22nXd9BYvcpPHaqGkHmRhVIBIyMjn0rrAPSjFAHN6XZXcfj7WNSltpI7a6sLNY2bHLqZCy8HqMj865I+FtTj06xvpdMmuH0/W765ezjm8qWWCZmAZGBHzAEMBkZGRXqVRRzwzPIIpUkMbbH2MG2N6HHQ+1AHPeGrW0N/c3sHh/UtPYxLELjUJCZJVyTtCl2IAPrjrXS1G08SzJC8sayyAlELAMwHXA6nFSUAFFFFABRRRQAUHofpRQeh+lAFuw/5B9t/1yX+QqxVew/5B9t/1yX+QqxQAUCigUAcg/8Ax9Xf/XxJ/wChGgUP/wAfV3/18Sf+hGgVu/hNIbi0UUVkdAgHNOqG6urextZLq7mjgt4xueSRtqqPc1KCCAR0NAhc1zeteFJNbu0lk17UYYop0uIYIli2xSJ90jKEnnJ5Peukrm9Y13VdH1KzR9NtJNOubyK0WYXTeaC/cptxxz3oBlm88OrqGk29pealeS3NtMJ4L8bEmjkHQjA29CRjGCOtLpfh5LG/m1G7vbjUb+aIQme4CjZGDnYqqAAM8nua2TwOfxNIrK43KwYeqnIoCyOOk+HkEmlSaOdc1VdIzmKzVk2xfNu67csAegJxXQa7oltr+jyabcySxo5VhNE2HRlIIZT2P+NaO5S/l7l39duRmlyN2AQSOoHagSSKUmj2E2iHR5LZWsDALcxdBsAwB+nWs/T/AA5cWVxbGbX9Tu7a1/1MErIo6YG9lUM+Pc/XNbuRu25GfT0pFZWYqrqSvUA5xQOxhXfhuSTU7m+0/Wr/AE17oL9oSHY6OQMBgHB2tjuPyp8elXmiaHZ6d4bFmqwfKft5dsg5JOV53EnNbLOiY3uq54G4gZp+MUBYydMPiH7S39rf2V5G07fsfmbt3HXdxjrWXN4JjltZtPGtakujzPufT9yFSCclA5XeEJ7ZrqsU13jRdzSIBnBJYdfSgGjI1Tw8moX9tqFreXGnX9vGYVuLcKd0Z52MrAhhnkehpdO0CLTNOvLeC8uTdXjtLPfOVaZ5CMbum0YGABjAx0rWZ1VdzEKo6knFBIADFlCnuTxQFkc1p/hGfT9Wm1FfEepyy3Do9wrpDtm2DABwnAxxxiprvwss2q3N/Z6rfacb0KLyK2KbZiBgN8wJVscZFaWm6vZatDJLaS7lSZ4CCMHcjbWwPrV4EE4BHHXnpQJJGFbaRfaD4esdK0CS1cW2U3ahu5Ukn+Dvk1Y0z+32uHGr/wBkm3KHb9kEm7dx13cYxmtTcu3duXae+eKNy7tu5Sw6rnkfhQBx0vw8gl0uTR/7d1VdIzmGyVk2xfNuAztywB6Amui1nSIdb00WVxLIiCWOXdHjOUcMOvuKv+ZHlV3pluVG4c/T1oLKoyzKoHUscAUDskOJyc1gWHhhtL1Oa5s9XvY7Oa4e5ksCEMZduWwxXcATzgGt8EHGCDnpisnVdaGnXmkwJGkov7z7KxD48v5GbPv93p70CdirdeFydVudQ03V73TJbsL9pW3CMkpAwG2uDhscZFWb7w/b3/hmTQpri6aB41jaZ5N8pwQclj1ORWr0pK0UDPnSFXgYPpisEeFLMeDG8Lief7I0JhMvG/Bbd6Y6n0rdpc0ezDnMbU/DyX15bX9te3FhqFtGYUuYApLRnBKMrAhhkA+xpLXwrp0Wj32nXJmvV1Fme9luGzJOxAGSRjGMDAGMYFbNHNHsw50c/aeE/KvbO4vtXv8AUlsMm0iudgWNsY3naoLsBkAn+daWn6RDp15qdzFJIz6hOJ5A2MK20LgcdMDvV7mlzR7MXOZ+i6TDoWjW2mW7u8UAIVpMZOWJ5x9av0UVolZWJbuFFFFMQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU1/uN9DTqa/3G+hpCOo0z/kE2f/AFwT/wBBFFGmH/iVWf8A1wT/ANBFFcwFGH79x/13k/nVXV5tThtV/sm0t7i4ZwpNzOYo41wcsSASccDA9asQn57j/rvJ/OsDxZol9rB0xrRbe5htZ2kuLC6laOK6UqQNxUH7pOQCCDSEc74g8T6teeBvF9qYbODU9Lj8q4ktrhjG0bpuDxtjO7BI2n86Zqo1jRdJ8KWun6XpNjHJq1uojtrh1VzsJUN8gyDj5jyeB1q5/wAINfNo/i61U6bbPrkUawRWyskUBVNpB4/UDn0FbPifQ7/UtN0n+zntjeaZew3aJcMVjl2AgqWAJGQx5welAEGq+LLqx1S00RBpUOqPa/armW7uzHbwru2gKcBnJIPGBwCapn4gyjRY7qPTori9TVk0qeC2uA6M7DIaN+AQQVPPTJz0q1f6HrJ1m18QWsWly6gbMWl7Z3DMIZAG3KUfaSpUkjkcg9qffaHq2q6dpiXf9mwXFtq0N86WwYRiNCflBIyzc9cAfSgAvfEHiHSYdLjvtJsJL/Ub82qQ21y2xV8tmUl2Ucgrzx06elMTxhdaXd6zaeJLS2hk06wGorJYyM6Sw5K4+YAhgRj3zWnr2kT6rqOg3EMiBdPv/tMocnLL5brge+WFZ+s+Ev7b13U57iZUsb7Rv7NO0nzFfzC27pjAyPyoAibxRr+nRadqGt6TYwabfTRQkW9wzzWhk4QyAgKwyQDt6Z71L/wkeu6nc6m/h/TbGey06d7Zmu52R7mVPvqm0EKAeMt1NVZdD8TazbadpetSaYmn2c0U089s7tJd+UQVGwqAmSATyfapItH8RaFPqsWgNpk1lqFzJdo127o9pLIPn4VSJFzyBlTzigBF8czahN4dj0XTo5zrdrNOn2mYxiBo9uQ2Ac4ywOO4HrWr4c1y91K71XTdUtYINR02VElNu5aKRXXcjLkAjjOQfSsrS/Bcmkal4Ve3ukkt9GtLmGVnyHleXB3AdMbsnrxkVZfRdbsdW8SappMlgbnUWtTbrdbtiiNdr78c9M4xQB0d/fQaZptzf3T7Le2iaaVvRVGT/KvMvDUV1oOtaL4i1AlW8VF0v1LcRTOTJbDB6YXKfjXaeLdFufEWkxaXFNHFazXMRvtxOXgVtzIuO7EAc9s1m658O9CvdGuYNL061sNQ2h7W6RSDDKpBVvzA/DNAG34n1z/hHNBm1AW/2h1eOKOMvsBd3CLubsuWGT6VlQeINfg8TSaLqVjpm6Owa/ae1uHwEBKhdrL1yO5HHI9Kj8eTzJ4CkgvHSKW78m2uJ1G6GHcw3u+Rny+oPQ4PbrWR4duPsutHRLAeHr2G9tZWnutIRlMG1cJ5hLOMHOAM+vFAGlpPjHVbvw9/wk+paXbWWhLYG6bbMz3DMADwuNoUnIGTnoeM09vFOvafDp2pazpFnDpd9LFEwt7hmmtPM4QyAqFYZIB29M96uWPhf/i3EPhXUJVz9g+ySyw8gHGNy5H481nzaF4l1mz0/SNal0xNPtZYpZ7i2d2lu/KIZRsKgJkgE8n2oAuJ4g1zVdR1FNC06xkstOuTayS3k7I08qj51TapwBkDJzznioL/AMZXTa7faZpo0iFtPCCd9TvPKEkjLu8uMAZ4BGWPGT0pYNH8RaHqGpjRTpk9jqN092pvHdHtZHHz8KCJFJGQMqeTzSS6DrGna9qGo6Vb6VfR6lskngvS0ZimVQpdGCtlSAMqe460ARHx5c3kHh1tI0kXM2tRz7Y5ZtoheIc7mAOVB3ZI9BjrWnZa9qUPiKx0TW7O1hnvLNpopraRmRpUb54xuAP3SrA/X0pr6Jf3OveHNTuJbNW06O4FwsCsqs0igDYOeOO5rP8AiZbvcaDYCynaDWft8SabLGOVlclW/wCA7C5P09qAN3w7rUmvx3t0kCx2Ud49vayhsmZUO1n+m4MB7Csjw14p1LxLcyz29tpiWEc8kMkBum+2Q7GZdzptwCSB8voetdFpWnW+j6TZ6Zari3tYliTtkAdT7nr+NcvJ4c1bUvE+m6ne2ej2Uljc+c19ZO/n3KAECNgVGFORnLN04oAg8Jaxqz6l4ruNUjs0sbXUpROy3Du0QjhThAVAK4Gc5HU8U8+NNag0GHxReaRZx6BKEkKJcM11FC5wsrDbtPUEqOg7mreneHdRs9W8QW0os5dF1m4kuJJPMYTqXjCFNuMY4znPTtWfJ4W8SXfheDwjdz6b/ZCLHBJfRu/nywIRhfLIwrEAAtuI68UAaU/iPXLrxLqWiaLpdnK9iIJGurqdkj2SLuxgAkt1wOmByaq3fje5k1LVItMGjrDpsrQyDUL3yZLiRVyyxrjgA8bj1NbOmaLNY+Ktb1RniNvfx2yxRqTuXy0KnP58VlDQdZ0fU9Tk0i30i8s9QuTdbb8sj28rAb8FVbepIzjgjJoA2V8Qw3Pgs+I7SMmJrBryOOQ4PCFtpI+mKxovGOpQeHJ/E+raVb2ujizSeCJJy1w7tgKGGNqhieOTjjNb9/Yz33hi705ngFzcWbwFkUrGHZCMgckLk+/FZ114YGo+AY/DVzcBJFs4oPOQbgsiBcMAcZG5QcGgChY+ML46tYWN+NFdtR3JbiwvfNMMoQsEk45BAI3LxkdOaoeFNa1yPSPE+oXtvZvFaX19Idtw7MHQZ8sArjZxwc59q6DSIfEaXcH9pWWgwwxjEs1pvaSY46qCoCDPPVqoaT4b1S0HiDS7hrM6RqU91Ok8cjeepmH3SmNvHPOeeKALN/4smsvDOgastojtqk9pC0ZcgR+djJB74zUSeItdv/EmqaRpOl2Zj0y6jjnurqdlUxsithVAJL8t7DA9ayj4X8T3uh6Lo97JpUcGkXdtKJonkZ7lIWGMgqAh2jpzz6Cum0bSJ9P1vxBeyyI0epXcc8SqTlQsSod3HXKmgDHfxT4hu012TStIsHj0i7mgY3FyytcCMBsIApw2D1PGcUtz48Wc6RBpQsYrjUbAX5k1KfyooIjgAHHLMSSAB6E1oaRoNxp9v4iilliY6nez3MWzOFWRAoDcdRjtWNbeENU0iHQbuw/s66vrDTF067t7osIp0GCGRtpKkNnqvINAEw8fM+kOY7O3uNXGorpkdvBcB4ZZmAYMsmPubDuPGRgioLWTV2+K+nLrFvZRzLotxtezkZkcebH2YAgj8c5q7qeg61qmlWkzHS7TVrC/W9tI4FbyPlBGx26nIJBYAduKlstH1ufxla+INVawiEdhLafZbVnfYWdGzvYDd0PYY469aAIPHWt3sP2Tw7o8NzLqWpqxd7VQZILdeHcZIAY52gkgAnPar/g6+0prCTRtPsJ9Nk0rbFLYXKgSRZGQxwSGDcncCc803WtI1QeILXX9Ea1kuorZrOe2umZEmiLBhh1BKsGHoQc0/wAPaNf2uqanrWsTQNqOoeWhits+VBFGDtQE8scsxJIHXpQBa0rWn1HVddsmgVBplykKMGz5gaJXyfT72K5+28c6he6Z4XnttKgkudd88CJpyqxGME53YORxzxn0q2NI8Q6b4j1e60ptOls9WeOV3umcPbSKgQkKBhwQAcZHNVNH8GX2mweDYpLm3kOhm4+0Fc/vPMVgNvHuM5xQBJN41v8ASbTxGur6ZC19otvHc7bKUtHPHJkKQWGVwQd2c4AzVtfFN3p/hm+17Vxpc9lBCJYpNKuGlEpJwE5GM5KjOe/QVLJo+pQ+JNb1ezezZryzt4IEuN23dGX3b8DoQ3GM/SsJPAEuoR68byGx0ePVLVIBa6YxeNZFYuJ2yqgtnAwAOAeaANQ+I9d0u70xvEGm2NvZ6lcJbI1rcM720r/cWQMoDAnjI6GsbTNcTw5ZeLrwW7XNxJ4jkt7e3DBPNlcRqq5PQZPJ9Aa1JtG8R69c6VDr39mQ2Wn3Md3I1nI7PdSJ93hgBGueSMt0FQXPgu+n07WY47u3jvJ9bGr2MhDFVZdpVXHX+Eg49aAIJn1tviZ4UXWbawjkFvfFHspGZTlFypDAEEcc9DntXoJ61yEWkeINQ8V6NrmqnTrdbCK4ja1tXeQ/vFA3byBnkdMDA7mutzQA7tSUmaM0ALRSZozQAtB6H6UmaQng/SgC7Yf8g+2/65L/ACFWKrWJ/wCJfbf9cl/kKsZoAWgUmaAaAORf/j6u/wDr4k/9CNAprn/Srr/r4k/9CNANbv4S4bj6XFM3Uu6s7M6Lo4b4nway3hTVpYL61TTFt182BrcmRjuGcPnjt27V0uhwazBbN/a97a3RYKYvs9uYtoxyDljmtJwkqFJFVkYYKsMg0uRSsxaXuOFcx44Um20Hg/8AIbtf/QjXS5rD1Hwh4e1a8a7vdNWadyGZ2lfqBgEAMAD9KdmDaKfjQC6vvDul3JI02+vzHdjdtEmEZkjY+jMOnfGKr2NlZ6D8RRYaVGttZ3GmPcXdrEuI43VwEk2j7pILD3xW9/YGkHRhpD2Mb2HXyXJYA5zkEnIOe+adpWi6Zool/s+1ETzEGWQsXd8dMsxJOPrSsyfM8w1OGKTwXN4m03SbaBWnE1tqlxeFr5283GeFxzyNu7pXc686aL4n0rxC7BLWVTp9856KrfNE5+j5H/A6lfwP4XllmkfRoGMxJZSW2Ak5JVc4U+4xWxfWNnqdjLY31us9rKu14n6EDkfyFFmBws81/ZfDXX/EcQaPUtV3Xe4fehiJCRj22x8+xJpNO8Oaha6nod9Y6RpGmJHKvm3MGotI15CyncrAoPMJ+9knORXoWyMw+SUUxbdmwjK7cYxj0xWPYeFNB0y7jurTTY45oyTFlmZYs9dik4X8AKOVhbzOe1jRP7Q8Tare/wBmWHiWIRxxPaS3GyexIXkICNo3fe6qfeuk8KXFpd+FtOmsJLqS1MWI2u23S4BIwx7kEEfhRqXhnRdWu/tV5Yh5yux5Edoy6+jbSNw+tS3Wlb9Pt7LTr2fSYoOF+xpH90DAXDKRiizGtGaozkV57oHhrSNb1zxVPqdmt55erOiRzMSifIhJC9Mn19hXVabpd3Y3LSXGu39+hXaI7hYgAcjn5EBzx696uWtjaWT3MltCsb3UpmmIz87kAFj+AFFmDdzivE6Xeo+PbHSv7NtNQtItPNxFZ3lyYopJN+0tgK28quOCOMk1p+FtAmhstX03VrSxGnSXAaHT45zcJACAWQ7gMDcNwGOM1uapo2m61HEmo2qzeS26J8lXjPqrAgj8DT9N0yw0e3Nvp9qlvEWLsF5LMepYnkn3JoswsrnK+AdA0eOzur2PTLQXdvqd3GkywrvjUSEAA9RgHGPSr/hxkTxB4y3MBtvUZsnGFMCcn261oJ4Z0WLVzqsVkI71n8xnSRgGfGNxUHaTz1xS3/hrRNT1AX15p8ctxtCsxJAcDoHAOGA980WYkcQCW/Z/BDEE23DA9P39dxpPhnSNFdriys1S6eMpJcsS0snc7mPJJIzUo0XTP7GGj/Y4/wCzgu37Pztxndj1681oZ4Iosxq3U8jj0TTk+DP9trbAapBAZ4bzJ82NllONrdh7Dit3xKL3VfHOn6a2m2mo2q6d9qjtLy5MUUkpbDNgKd5UY4I4yTXXjRtN/sT+xvsaf2cUKG3BO3aTnHXPWjU9F0zWIYYr+0WYQNuhbJV4z/ssCCPzp2YjiZ7fWfDngTxSALezVWD20FncmY2aNtEgBIBAGSwHbNWtR8O6Ho2r+DpNKtYYWkvwu+NuZl8lzubn5j0+Y+vXmutsNH0zS7J7OzsooreQkyx43eYSMEsTksSO5qjZeD/Dun3UV1baXGk0D74X3u3lnnhcngcngcUKLEzdFFJmjNboyYtFJmjNMBaKTNGaAFopM0ZoAWikzRmgBaKTNGaAFopM0ZoAWikzRmgBaKTNGaAFopM0ZoAWikzRmgBaKTNGaAFpH+430NGaa5+RvoaQjqNM/wCQVZ/9cE/9BFFJpp/4lVn/ANcE/wDQRRXMBmwt89xz/wAt5P51Bf6xZ6ZLZR3cpRr24FtBhSd0hBIHHTgHk8U2GT5p/wDrvJ/OuW8bvnUvB/P/ADHYv/Rb0hHb7qN1cFZ3mveKJNXvLPWv7Mgs7yWztII7dJA7R8FpSwJILdhjAHWkj8Syax4V0bUptbTQzeKwmWKESyyOuVIiDA8ZBP3SaAO+3e9G6vMrLxdqqaVb6jcXfn2lhrD6ffymARmWBsKkxBAKlSy5HHfirWueMNSspPEF7Y7ZLTTvJsIEKZEl3Iw3McckIGUYHU5oA9D3UFveuE03Udbt9btIPP1LUra4SQXBvNO+ziBwuVZWCj5SRtwcnkc1D4S1+7vdQig1fWbqHVnRmuNIubJYVBGf9S2Msox13NkelAHoO7vVXTNUs9Y0+O+sJxNayZCuARnBIPX3BoMhwTntXnPw+1HxBD4IsEstDtri3Vpgkr6h5Zb96/8ADsOPzoA9GsdUs9SNyLScS/ZZ2t5sA/LIuMr+GRVrdXk2k6/f6T4e16aKGKHU7zxM9pGrN5kcMkhQZJ43Acn3xXQTXOt6Hr2l6fPrL39tq3m24kmgjSS3mCFlZdoAKnB4IOOOaAO63e9Ju968vuPGmtv4V0mS02Nq8XnS6mmwHKWx2zDHYscYwO9dPp+tzav4svUtZ1Ok2lpCOFH7yaT5859k28f7VAHUkggg8g9QaZDFDbqVhjSNSckIoAJ/CuHub3XNT8S+ItOtNY+wQWEMEkBS3R23vGSQSw+7kdOvuKzbbxFrw0rw34ln1JWi1S7ht5tOWBREiSkqCrfe3DGeTjnGKAPTc0bq868Y67d6WdTvLXxUqXNlH5sOmw2ayJgDJWY4JBbnnK4yKuzanq+qeLo9KtNR+wWcmkR3jskKvIGLkfKW4HbqD0oA7jPpRu4615pH4p1qx08Xl5dLPb6TrL6dqMoiVfNgO0LKcfdKllJxx1ro9M1W61PxTq6xSj+ytPCWqgAHzLj70hz1woKrj1zQBuRaraT6tdaZHITd2sccsqbSAFfO3nofumrZ2kqSASOQfSvOdS1z/hH/ABP4z1cRiVrXSrR1Q9GbMgAPtkirN7ea/wCHLfTNUvNZ/tCO5uYYLy1a3REUSnbuiKjcNpI4JORQB326lDe9eceLteu9JOp3lp4oUXFkvmxaZFZiRNoAJWVsFgTzzlccVpQ+J5dP13VItWlxYGwXVLJioG2NV/ex5HUg4PrhqAO0z70bsd68yXxHr8p0bTLq4urW6vbSTUbqW0sxNJEhfEcSrggYBGWIPT3qebXfE8Og3oSG9la3vo41vfsG2drVgCzrCeGdfu9Md8UAej7veorm6jtbaa4mbEUSNI564VRk/oK57wrqUOoaa7wa4+rIsm3zJYRFLEcD5HUAc/UDrXPS3eteItI8R6kurNZWds11a29nHAjLIsalWaRiN2WOeARjigDsIPE2lXD6THHdZfVojNZKUYeYgUMT044I64q7qOqWuk6Zc6jeSGO2t4zJKwUthR1OBya4Lw5qtza2nw/sUcLa3OlSNOu0EtshQrg9R17Vk6td614i+GGr+IptXMMF1bStHp6W6GJYQxUAsRuLkDO7OOelAHrQmQxCTcFQruyeABjOap3uuWFhYwXs1wDbXEkccUiAsGMhATGOxJHNZPiFrg+C9Q+zXLW8gsHYOFDHAQkjn1HGe1cdfQ30Xwt0LfqP2iR59NMHmQqoi+ZMD5cFh0688UAeq596N3vXDSarqnh7xOtrf6m+pWVxYXF2Q8CRtE8O0kKVH3SG6HOPWsmw8ReI7nTbDW45NRup7l4pX01NMxb+S5GVSTG7Kqc7i2DjpQB6hu96rahqNvpem3OoXchS2to2llYAnCgZJwOTXES+Lb7RrXxNaX7rc6jYSBrDCBftCTcQDA6kOdp+lXPFS3cPwv1aK+nE94ulus8oUKHfZ8xAHAGaAOximWaGOVDlJFDKfUEZFP3e9eem91rQrrwxNPq/2u21GWOzntPs6Ike6MlWjI+YYK4OSc1U8V+JLzRft+oW3idZp7KQMNLitA0Xl5AKSOASGwc53D6UAem7vejd71xDeKpNG1LxFFq8oaG1txqVicAFoCMFOOpVxj1+YVk/2/r73mm6JeXd3b3X9ni/v57GyEsm53IWJV2kKqjOSQenvQB6bu96pafq1pqguvsspf7LcPbS/KRtkXG4c9evWsbwvf6pc2VymqpN5kFw0cM8tv5LXEWAVcp2PJBxxxXIQ67d6LpGufYR/pV54oltI38oyeWXIy2wcsQAcDucUAeqhsnArKs/Euk34u3trxWhtCRNOVZYlwcHDkBTgg5wTiuXsbzW21CawF7qc9nNaSOL28sFhe2mBGAPlCsGBOARxjrXJQR6knwBEy6u4ia1XbALePCr5pBXdjPOc568UAesar4h0zRBD9vuTG85IijSNpHfHUhVBOB61Y07U7PVbGG+sLlLi2mGUkQ8H1+h9qx7ifVNH8Pyyws2r6iuBCGjWLJYgAHaPujqe+BT/Dulf2DpP2R7n7RM80lxPKF2h5ZGLMQvZcngelAG/u96Td71W82jzPegCzu96N3vVbzPejzPegCzu96N3vVbzPejzPegCzu96C3B5qt5nvSGTg80Aati/wDxL7bn/lkv8hVjf71m2Mv+gW3/AFyX+QqfzaALe/3oD+9VPNpRLQBzTt/pV1z/AMvEn/oRo3VXd/8ASbr/AK+JP/QjRvrqjsMsbqN1V99G+mMsbqN1V99HmGgCxuo3VX30b6ALG6jdVffRvoAsbqN1V99G+gCxuo3VX30b6ALG6jdVffRvoAsbqN1V99G+gCxuo3VX30b6ALG6jdVffRvoAsbvejdVffR5hoC5Y3Ubqr76N9AFjdRuqvvo30AWN1G6q++jfQBY3Ubqr76TzKALO6jdVbzKPMoAs7qN1VvMo8ygCzuo3VW8yjzKALO6jdVbzKPMoAs7qN1VvMo8ygCzuo3VW8yjzKALO6jdVbzKPMoAs7qN1VvMo8ygCzuo3VW8yjzKALO6jdVbzKPMoAs7qR2+Rvoar+ZSNJ8jfQ0mI67TX/4ldp/1wT/0EUVV06X/AIllp/1xT/0EUVzAZGy8jeULDG6tIzKTLjgn6Vm6to9xq8+mTSxhDp92t2gWbO5gpGDlenNFFIRnzeF9TW6vX0zUZ9Nt79zJdQRMjguRhnQsuUY9+vPNIfCVxaS6XLo7/wBnPp9s1pHhllDRMQTkMv3sjO7880UUXAmtfCzQ6PqumTK1zDqckks7TTgsWcAE5Cj0zSWvhKODwm3h2a3Nxby7mmme4xI8jNuLk7fvZwc+wooouBNYaV4gtrhXu9ZuLyNEKJGTHHnIxuYqmWYfgPaoLfw5qp1OzvtT1CTUGsd5tFfZH5bMu0sxVfmOPoPaiii4Gnpdpqen6ZFa3Er30qZ3XE8w3vkk84Xt0/CodA0e48P6JBpcEYljhLFXeXBO5i3ZfeiilcCh/wAIgsmm6pYyo7R6hetfeYs4V4ZSQQUO3sVGKktPDupLq1tqWqXr6ncWistqHKRJFuGGbCryxHGfrxRRTuA7T/DH9na5q2qpCHk1LAeJ5hsjH8QUbf4jyfen+GvD0nhjTGsbaMzBpDI0ks+WPAAH3egUAD2FFFK4E1vo9xb6xqmoqmX1FIkkQyjCbFKjHy+/eqA8JyDw9o+jjIi0ueGeOTzRucxnIB+XGDmiincCG48HX00er2iX8sOm6pJJLPbIybt7jDYcrnacDj9cVpW2hz22uLqirulWwSxCGUbdqtu3fd60UUXAztS019E8M+JGazF3HfvNcPC0w5eRQu0cdM45rQ8M6Pd+H/DllpogjeSJMzSedzJI3Lt07kmiigBk/hz7XqGq3N1bLLHqdrHazwmbChE3dCBnJ3H8hVKLwnqTyWSajqVxqFnYSLLbW0rIvzKPlMjKgL46jOOeuaKKLgNn8G301vq1imoTw6bqcks01ujJu3yfe+crnbnt+GcVZ17wkPEFvp0N1EVFi4wUmAMseAGjb5futgZ+lFFFwLGsaFeaneWuoW0jafqNqGWK4hkDAo2MoysuGXgHHbHFINK8QDT2iGs3H2wy+b9qJjIHGNgTZjZ7dfeiii4Emi6Ld6RJe3MpF3e30olubh3CbiFCqAqrgAAf/XrNfwjerLqEdrfT2unai7y3FnG6Y3uMMVcpuUHuB+lFFK4Fqz8My2UmgOgLHRbZ7eENIP3gZAuW+XrgdqzZPAt42k3mjJqVzFo1xv22iun7rcSSA+3cVyc7f6UUUXGdPPa3FzpsljLbKYpYTC+JsHaRg9vSsGDwnf8A9g22k3l9JcxWs8EsDEohRYSCqcLz0GSeaKKdyTUudIuLvXbTVZYUL20EsAi83Kusm3Ofl/2azbDwxq2mLDa2mr3UelwMGitN6FlUHPl+YU3bO2OuOM0UUXAq6jpU2r/EfTLhrBAdLtTM7+fxIzN+7UjH8JDN06kV0Gsafdaxot7pksSxx3ULQs6y8qCMZHy0UUXAr3+hz3/9jh02f2Xcx3Me2b77IpUA/L05rIn8EXk+m6jpP9oTppV7JJMbdWTcru245fblhu5wfpmiii4F/XPCw1+602e5h2tYuDhJsCZMq2xvl+7uVT+FS6toV9f6jDqlnM2n6lDGYRPG6urxk52OjLhhnkehooouBe0611SytjHcTPfSs5ZpppVU89gFUAAelZD+EBNp2oWciP8A6XfnUFlScK8E2QQyHb2IHX3ooouMu6fpmu20skl/qkl+WjMaxnZEig9yFTlvfP4VTj8JbPBH/CKsrtaeQYfN84B/vbgfu4yDRRRcRZOk67Jon2KXVpvtW9W+2xFEfAIO0KFwAQMfjWxm8/59o/8Av9/9jRRQAZvP+faP/v8Af/Woze/8+0f/AH+/+tRRQMM3v/PtH/3+/wDrUZvf+faP/v8Af/WoooAM3n/PtH/3+/8ArUZvP+faP/v9/wDWoooAM3n/AD7R/wDf7/61BN7ji2j/AO/3/wBjRRQMuWztFawxuBuRApweMgVL51FFAB51KJqKKAKT6fYySNI1upZzuY5PJ/Om/wBm2H/Psv8A30f8aKKLsA/s2w/59l/76P8AjR/Zth/z7L/30f8AGiindgH9m2H/AD7L/wB9H/Gj+zbD/n2X/vo/40UUXYB/Zth/z7L/AN9H/Gj+zbD/AJ9l/wC+j/jRRRdgH9m2H/Psv/fR/wAaP7NsP+fZf++j/jRRRdgH9m2H/Psv/fR/xo/s2w/59l/76P8AjRRRdgH9m2H/AD7L/wB9H/Gj+zbD/n2X/vo/40UUXYB/Zth/z7L/AN9H/Gj+zbD/AJ9l/wC+j/jRRRdgH9m2H/Psv/fR/wAaP7NsP+fZf++j/jRRRdgH9m2H/Psv/fR/xo/s2w/59l/76P8AjRRRdgH9m2H/AD7L/wB9H/Gj+zbD/n2X/vo/40UUXYB/Zth/z7L/AN9H/Gj+zbD/AJ9l/wC+j/jRRRdgH9m2H/Psv/fR/wAaP7NsP+fZf++j/jRRRdgH9m2H/Psv/fR/xo/s2w/59l/76P8AjRRRdgH9m2H/AD7L/wB9H/Gj+zbD/n2X/vo/40UUXYB/Zlh/z7L/AN9H/Gj+zLD/AJ9l/wC+j/jRRRdgH9mWH/Psv/fR/wAaP7MsP+fZf++j/jRRRdgH9mWH/Psv/fR/xo/syw/59l/76P8AjRRRdgH9mWH/AD7L/wB9H/Gj+zLD/n2X/vo/40UUXYB/Zlh/z7L/AN9H/Gj+zLD/AJ9l/wC+j/jRRRdgH9mWH/Psv/fR/wAaP7MsP+fZf++j/jRRRdgH9mWH/Psv/fR/xo/syw/59l/76P8AjRRRdgH9mWH/AD7L/wB9H/Gj+zLD/n2X/vo/40UUXYB/Zlh/z7L/AN9H/Gj+zLD/AJ9l/wC+j/jRRRdgH9mWH/Psv/fR/wAaP7MsP+fZf++j/jRRRdgH9mWH/Psv/fR/xo/syw/59l/76P8AjRRRdgH9maf/AM+y/mf8aP7M089bZf8Avo/40UUrsC7G6xRrHGoVEAVVHQAUUUUgP//Z" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">PAL example<br /></td></tr></tbody></table><p style="text-align: center;"></p><p>In the PAL example above, note how the blue lines start with a hash, to generate Python comments. The left side, is the one shot example given to the LLM, which shows it how to reason out a problem and generate Python variables which will do the calculation. On the right side, you can see how the LLM generates a completion which is basically a script which can be fed to a Python interpreter to do the calculation accurately.</p><p>This process can be automated with the Orchestration library. It decides what action to take based on the LLM output. It can also manage the flow of information and initiation of calls to the external apps. <b>The LLM is the reasoning engine which creates the plan which the orchestrator will interpret and execute</b>.<br /></p><h3 style="text-align: left;">ReAct: Combining reasoning and action</h3><p><a href="https://arxiv.org/abs/2210.03629" target="_blank">ReAct</a> is a prompting strategy that combines chain of thought reasoning<br />with action planning. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="200" 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" style="margin-left: auto; margin-right: auto;" width="154" /></td></tr><tr><td class="tr-caption" style="text-align: center;">The sequence of an example prompt<br /></td></tr></tbody></table><p style="text-align: left;"> The example prompts start with:</p><p style="text-align: left;"><b>Question:</b> Problem that requires advanced reasoning and multiple steps to solve. Eg: "<i>Which magazine was started first? National Geographic magazine or Popular Mechanics?</i>". </p><p style="text-align: left;">The example then includes a Thought, Action, Observation trio of strings.</p><p style="text-align: left;"><b>Thought:</b> A reasoning step that identifies how the model will tackle the problem and identify an action to take. Eg: "<i>I need to search for National Geographic magazine and Popular Mechanics and find which one was started first</i>". It demonstrates to the model how to tackle the problem and identify an action to take.</p><p style="text-align: left;">To identify which external app to invoke, it has to identify which action to take, from a pre-determined list.</p><p style="text-align: left;"><b>Action:</b> An external task that the model can carry out from an allowed set of actions. In this case, the authors created a Python script to interact with Wikipedia. The allowed actions were <i>search[entity]</i>, <i>lookup[string]</i> and <i>finish[answer]</i>. Since the Thought prompt identified it needs to search for two magazines, the action would be <i>search[National Geographic]</i> and <i>search[Popular Mechanics]</i>.<br /></p><p style="text-align: left;"><b>Observation:</b> This is where the new information provided by the external search is brought into the context of the prompt for the model to interpret. Eg: "National Geographic is an American monthly magazine founded in 1888".</p><p style="text-align: left;"><b>Cycles: </b>The prompt then repeats the Thought-Action-Observation cycle as many times as necessary, to obtain the answer. The second cycle will find information about Popular Mechanics and the third cycle will have a Thought <i>"National Geographic was started in 1888 < 1902, when Popular Mechanics was started"</i>. The next action would be <b>finish[National Geographic]</b> to indicate the end of the process.</p><p style="text-align: left;"><b>Inference process:</b> For inference, you'll start with the ReAct example prompt. Depending on the LLM you're working with, you may find that you need to include more than one example and carry out future inference. Next, pre-pend the instructions at the beginning of the example and then insert the question you want to answer at the end. The full prompt now includes all of these individual pieces, and it can be passed to the LLM for inference. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="331" 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" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Click to view larger<br /></td></tr></tbody></table><p style="text-align: left;"><span><span>ReAct bridges the gap between reasoning and acting in LLMs, yielding remarkable results across language reasoning and decision making tasks, by interleaving reasoning traces and actions. </span></span>ReAct outperforms imitation and reinforcement learning methods in interactive decision making, even with minimal context examples. It not only enhances performance but also improves interpretability, trustworthiness, and diagnosability by allowing humans to distinguish between internal knowledge and external information.</p><p style="text-align: left;"><b>Image: </b>The figure provides a comprehensive visual comparison of different prompting methods in two distinct domains. The first part of the figure (1a) presents a comparison of four prompting methods: Standard, Chain-of-thought (CoT, Reason Only), Act-only, and ReAct (Reason+Act) for solving a HotpotQA question. Each method's approach is demonstrated through task-solving trajectories generated by the model (Act, Thought) and the environment (Obs). The second part of the figure (1b) focuses on a comparison between Act-only and ReAct prompting methods to solve an AlfWorld game. In both domains, in-context examples are omitted from the prompt, highlighting the generated trajectories as a result of the model's actions and thoughts and the observations made in the environment. This visual representation enables a clear understanding of the differences and advantages offered by the ReAct paradigm compared to other prompting methods in diverse task-solving scenarios.</p><p style="text-align: left;"> <br /></p><h3 style="text-align: left;">LangChain</h3><p style="text-align: left;">The <a href="https://github.com/hwchase17/langchain" target="_blank">LangChain</a> framework provides you with modular pieces that contain<br />the components necessary to work with LLMs. </p><p style="text-align: center;"><img alt="" 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" /> <br /></p><p style="text-align: left;">These components include:</p><ul style="text-align: left;"><li style="text-align: left;"><b>Prompt templates: </b>for many different use cases that you can use to format both input examples and model completions. </li><li style="text-align: left;"><b>Memory: </b>that you can use to store interactions with an LLM. </li><li style="text-align: left;"><b>Pre-built tools:</b> that enable you to carry out a wide variety of tasks, including calls to external datasets and various APIs. </li></ul><p style="text-align: left;">Connecting a selection of these individual components together results in a <b>chain</b>. The creators of LangChain have developed a set of predefined chains that have been optimized for different use cases, and you can use these off-the-shelf to quickly get your app up and running. Sometimes your application workflow could take multiple paths depending on the information the user provides. In this case, you can't use a pre-determined chain, but instead we'll need the flexibility to decide which actions to take as the user moves through the workflow. </p><ul style="text-align: left;"><li style="text-align: left;"><b>Agent:</b> can be used to interpret the input from the user and determine which tool or tools to use to complete the task. Agents can be incorporated into the chain to plan and execute one or more actions.<br /></li></ul><p style="text-align: left;">LangChain currently includes agents for both PAL and ReAct, among others. <br /></p><p style="text-align: left;">Additional info:<br /></p><ul style="text-align: left;"><li><b><a href="https://github.com/hotpotqa/hotpot" target="_blank">Hot Pot QA</a>:</b> A multi-step question answering benchmark. That requires reasoning over two or more Wikipedia passages.</li><li><b><a href="https://huggingface.co/datasets/fever" target="_blank">Fever</a>:</b> A benchmark that uses Wikipedia passages to verify facts. </li></ul><h2 style="text-align: left;">Overview of the infrastructure needed for a Generative AI application</h2><p style="text-align: center;"><img alt="" height="293" 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width="640" /></p><p> </p><h2 style="text-align: left;">Responsible AI<br /></h2><p>With the growth of AI comes the recognition that we must all use it responsibly. </p><p>There are lots of challenges. Three major ones are:</p><ul><li><b>Toxicity: </b>You can start with curating the training data. You can also train guardrail models to detect and filter out any unwanted content in the training data. We also think about how much human annotation is involved when it comes to training data and training annotations. We want to make sure we provide enough guidance to those annotators and also have a very diverse group of annotators that we're educating so that they can understand how to pull out certain data or how to mark certain data. </li><li><b>Hallucinations:</b> We can educate the users that this is the reality of this technology and add any disclaimer so they can know that this is something you should be able to look out for. Also, you can augment large language models with independent and verified sources so you can double check against the data that you're getting back. You also want to make sure that you develop methods for attributing generated output to particular pieces of training data so that we can always trace back to where we got the information. We always want to make sure we define what the intended use case is for versus what the unintended use case is.</li><li><b>Intellectual property issues:</b> This is likely to be addressed over time by a mixture of not just the technologies, but also with policymakers and other legal mechanisms. Also, we want to incorporate a system of governance to make sure that every stakeholder is doing what they need to do to prevent this from happening in the near term. There's a new concept of machine unlearning in which protected content or its effects on generative AI outputs are reduced or removed. So this is just one approach that is very primitive in research today. We can also do filtering or blocking approaches that compare generated content to protected content and training data and suppress or replace it if it's too similar before presenting it to the user. </li></ul><p>Defining use cases is very important. The more specific, the more narrow the better. One example where we actually use gen AI to test and evaluate the robustness of a system is when it comes to face ID systems. We actually use journeys of AI to create different versions of a face. For example, if I'm trying to test a system that uses my face to unlock my phone, I want to make sure I test it with different versions of my face, with long hair, with short hair, with glasses on, with makeup on, with no makeup on. And we can use gen AI to do this at scale.<br />And so this is an example of how we use that to test the robustness. Also, we want to make sure we assess the risk because each use case has its own set of risks.<br />Some may be better or worse. Also, evaluating the performance is truly a function of the data and the system. You may have the same system, but when tested with different types of data, may perform very well or may perform very terribly. Also, we want to make sure we iterate over the AI lifecycle. It's never a one and done.<br />Creating AI is a continuous Iterative cycle where we want to implement responsibility at the concept stage as well as the deployment stage and monitoring that feedback over time. And last but not least, we want to issue governance policies throughout the lifecycle and accountability measures for every stakeholder involved. </p><h2 style="text-align: left;">Conclusion </h2><p>As model capabilities increase, we'll also need more scalable techniques for human<br />oversight, such as constitutional AI, which I discussed in a previous lesson. Researchers continue to explore scaling laws for all steps of the project lifecycle, including techniques that better predict model performance so that you can make sure resources are used efficiently, for example, through simulations. And scale doesn't always mean bigger, research teams are working on model optimizations for small device and edge deployments. For example, llama.cpp is a C++ implementation of the LLaMA model using four bit integer quantization to run on a laptop. </p><p>Researchers are looking into developing models that support longer prompts and contexts. For example, to summarize entire books. Models will also increasingly support multi-modality across language, images, video, audio, etc. Researchers are also trying to learn more about LLM reasoning and are exploring LLMs that combine structured knowledge and symbolic methods. This research field of neurosymbolic AI explores the model's abilities to learn from experience and the ability to reason from what has been learned.</p><p>To conclude, you could even ask ChatGPT about <a href="https://a16z.com/2023/01/19/who-owns-the-generative-ai-platform/" target="_blank">what the future holds</a> :-)<br /><br /></p><p style="text-align: left;"></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-30558312132036279172023-07-09T18:00:00.006+05:302023-07-22T17:41:44.242+05:30Generative AI with Large Language Models: Part 2<p>Continued from <a href="https://nrecursions.blogspot.com/2023/07/generative-ai-with-large-language.html" target="_blank">Part 1</a>, this blog post continues with what I learnt from the Coursera course on Generative AI.<br /></p><p><i><b></b></i></p><blockquote><i><b>LICENSE:
Images shown in this blog post and following parts of the blog are
screenshots taken from the <a href="https://www.coursera.org/learn/generative-ai-with-llms" target="_blank">Generative AI course</a>. You may not use or distribute these
or the text content for commercial purposes. It was created by <a href="https://www.deeplearning.ai/">DeepLearning.ai</a>, and is licensed under a <a href="https://creativecommons.org/licenses/by-sa/2.0/legalcode" target="_blank">creative commons license</a>. </b></i></blockquote><p></p><p>Here are some of the main points:</p><h2 style="text-align: left;">Instruction fine-tuning</h2><div><b>Limitation of in-context learning: </b></div><div><ul style="text-align: left;"><li>Poor results for smaller LLMs, even when 5 or 6 exemplars are given.</li><li>Multiple examples take up more space in the context window. </li></ul><div>So, fine-tuning is done to further train the model to improve performance.</div></div><div><b>Pre-training is self-supervised learning</b>, where an LLM is trained using vast unstructured text data. </div><div><b>Fine-tuning, is supervised learning</b>, where labeled <b>task-specific examples</b> (prompt-completion pairs) update the LLM's weights. It improves the model's ability to provide good completions for a <b>specific</b> task, often <b>requiring just 500 to 1000 examples</b>.</div><div>Instruction fine-tuning is just one of the ways to do it.</div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiCRbKdUvKEbYHFw5p5Y2j6AnUr7gMJoo9eUvEVzN0ssvzQz2prWQPuBOSLtHaTm1grD79_JHK-9zd3dRR3uQR_IxTjWNi4EAOhOujj8MLsvcaftlUvQBtgg_kUrSBXMNEqHavFsKfrtq-XyAOripcDE6LrHmSNPHcEuf8pMRlqXVA1zXtTH_-8zUrcNP4" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="415" data-original-width="890" height="298" src="https://blogger.googleusercontent.com/img/a/AVvXsEiCRbKdUvKEbYHFw5p5Y2j6AnUr7gMJoo9eUvEVzN0ssvzQz2prWQPuBOSLtHaTm1grD79_JHK-9zd3dRR3uQR_IxTjWNi4EAOhOujj8MLsvcaftlUvQBtgg_kUrSBXMNEqHavFsKfrtq-XyAOripcDE6LrHmSNPHcEuf8pMRlqXVA1zXtTH_-8zUrcNP4=w640-h298" width="640" /></a></div><br /><br /></div><div>So if you are fine-tuning the model to perform summarizations, you'd use exemplars like:</div><div></div><blockquote><div><i><b>Summarize the following text: </b></i></div><div><i><b>[EXAMPLE TEXT]</b></i></div><div><i><b>[EXAMPLE COMPLETION]</b></i></div></blockquote><div></div><div>If you were fine-tuning for translation tasks:</div><div></div><blockquote><div><i><b>Translate this sentence to:</b></i></div><div><i><b>[EXAMPLE TEXT]</b></i></div><div><i><b>[EXAMPLE COMPLETION]</b></i></div></blockquote><div></div><h3 style="text-align: left;">Full fine tuning</h3><div>All the model weights are updated. It needs enough memory and compute to store and process all the gradients, optimizers, etc. </div><div>There are many curated libraries of such prompt templates. One example is shown <a href="https://github.com/bigscience-workshop/promptsource/blob/main/promptsource/templates/amazon_polarity/templates.yaml" target="_blank">here</a>.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhlKnxF5dfTloAfNvznu__emBxRdHND0vUb1_rtG_h4aP7NQqisd0_bkrLWweUTXZMRjRgdzCGhOPa4iUSV_5j-_5RL6bIaoz-8IWl4LBwr4OnGq-rlmjKwlt0Bk7Kk6eDtD4EC86tACWbxT30mkg9cK-MS9nyMfFdyloQHdxfQkNg6L47GdLA1iq137EE" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="352" data-original-width="937" height="240" src="https://blogger.googleusercontent.com/img/a/AVvXsEhlKnxF5dfTloAfNvznu__emBxRdHND0vUb1_rtG_h4aP7NQqisd0_bkrLWweUTXZMRjRgdzCGhOPa4iUSV_5j-_5RL6bIaoz-8IWl4LBwr4OnGq-rlmjKwlt0Bk7Kk6eDtD4EC86tACWbxT30mkg9cK-MS9nyMfFdyloQHdxfQkNg6L47GdLA1iq137EE=w640-h240" width="640" /></a></div><div><br /></div>The instruction data is divided into training, validation and test splits.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhuoBm3yDdwAbOHhfJgx5e0KficzFVGGKO7tTZl950sz3Q_OrlosYEzkqGtyqiVvUdWLa7UhRPsZerFQvnaf-ZgwcraTS7wm0QEwfzsiz53rcnpcWNCouSbVQLLAvHkoVLRXY8rboEOXCsaG0obZP_CJLO6M3GhbTZTKFLyG8tkinOEGRVUlJaHZD1To9g" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="372" data-original-width="680" height="219" src="https://blogger.googleusercontent.com/img/a/AVvXsEhuoBm3yDdwAbOHhfJgx5e0KficzFVGGKO7tTZl950sz3Q_OrlosYEzkqGtyqiVvUdWLa7UhRPsZerFQvnaf-ZgwcraTS7wm0QEwfzsiz53rcnpcWNCouSbVQLLAvHkoVLRXY8rboEOXCsaG0obZP_CJLO6M3GhbTZTKFLyG8tkinOEGRVUlJaHZD1To9g=w400-h219" width="400" /></a></div><div><br /></div><div><b>Fine tuning steps:</b></div><div><ul style="text-align: left;"><li><b>Select prompts</b> from your training data set and <b>pass them to the LLM</b>, which <b>generates completions</b>. </li><li><b>Compare LLM completion</b> <b>with</b> the response specified in the <b>training data</b>. The LLM output is a probability distribution across tokens. So you can compare the distribution of the completion and that of the training label and <b>use a standard crossentropy function to calculate loss </b>between the two token distributions. </li><li>Then use the calculated loss to update your model weights in standard backpropagation. This is done for many batches of prompt completion pairs and over several epochs.</li><li>Update the weights so that the model's performance on the task improves. </li><li>Define separate evaluation steps to measure LLM performance using the holdout validation data set. This will give the validation accuracy.</li><li>After completing fine tuning, perform a final performance evaluation using the holdout test data set.</li><li>This will give the test accuracy. </li></ul></div><div>Fine-tuning results in a new version of the base model, often called an <b>instruct model</b> that is better at the tasks you are interested in. </div><br /><h3 style="text-align: left;">Avoiding Catastrophic Forgetting</h3></div><div><div>Catastrophic forgetting happens because the full fine-tuning process modifies the weights of the original LLM. It leads to great performance on the single fine-tuning task, but degrades performance on other tasks. </div></div><div><div><b>1. Decide if catastrophic forgetting actually impacts your use case</b>. If all you need is reliable performance on the single task you fine-tuned on, it may not be an issue that the model can't generalize to other tasks. If you need the model to maintain its multitask generalized capabilities, you can perform fine-tuning on multiple tasks at one time. <b>Good multitask fine-tuning may require 50-100,000 examples</b> across many tasks.</div><div><b>2. Perform Parameter Efficient Fine-Tuning (PEFT)</b> instead of full fine-tuning. PEFT is a set of techniques that preserves the weights of the original LLM and trains only a small number of task-specific adapter layers and parameters. </div></div><div><br /></div><h3 style="text-align: left;">Multi task instruction fine tuning</h3><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEj60fCJ7L7INFlUEd_IaEQkD1gZc9yCGLsG0UOMUDDPphDelt-JceUZt2M9I-cDgH9qWeBk5lSFNx96PyJWIg2D1b8lpvJctXEoLlMSNzhf9P5iYcow2M-vur654PZxadjSJ138Cro1hjkIeCa6vZWyEeTt7wZOtyWBualHIxUpERueRB-0f7VIVndjkuM" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="309" data-original-width="888" height="222" src="https://blogger.googleusercontent.com/img/a/AVvXsEj60fCJ7L7INFlUEd_IaEQkD1gZc9yCGLsG0UOMUDDPphDelt-JceUZt2M9I-cDgH9qWeBk5lSFNx96PyJWIg2D1b8lpvJctXEoLlMSNzhf9P5iYcow2M-vur654PZxadjSJ138Cro1hjkIeCa6vZWyEeTt7wZOtyWBualHIxUpERueRB-0f7VIVndjkuM=w640-h222" width="640" /></a></div><br /><b>F</b>ine tuned <b>LA</b>nguage <b>N</b>et (FLAN) is a specific set of instructions used to perform instruction fine tuning. When the T5 model is fine tuned with FLAN, it's called FLAN-T5. Similarly, there's FLAN-PALM. The image below shows how many datasets FLAN-T5 was trained with for multi-instruction.</div><div>Paper: <a href="https://arxiv.org/abs/2210.11416" target="_blank">Scaling Instruction-Finetuned Language Models</a>.</div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgZvGf6VL6g6UVQkB9luJxNuDciHLekCjQ9q5-9o1ZHm4Bl9OlbPJve7j2SpL1GJrJE1Ra_EbTtHDexQs_YqZvqgWhOgHYTeil6ahzhnZ0bK8ck3UXPlBLMHLTREEZ1cTPpmxG5LeCSqdkqzTzu0fqHyB19BD33OBdJPqKVLquZ8TvRihm4j7wzDOQU4ug" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="280" data-original-width="930" height="192" src="https://blogger.googleusercontent.com/img/a/AVvXsEgZvGf6VL6g6UVQkB9luJxNuDciHLekCjQ9q5-9o1ZHm4Bl9OlbPJve7j2SpL1GJrJE1Ra_EbTtHDexQs_YqZvqgWhOgHYTeil6ahzhnZ0bK8ck3UXPlBLMHLTREEZ1cTPpmxG5LeCSqdkqzTzu0fqHyB19BD33OBdJPqKVLquZ8TvRihm4j7wzDOQU4ug=w640-h192" width="640" /></a></div><br />It's important to <b>use datasets that are closer in similarity to the real-world application</b> of the model. This can often involve an additional iteration of fine-tuning with human-curated examples from actual examples of where the model was used and where it was seen that the model performed poorly.</div><div><br /></div><h2 style="text-align: left;">Model evaluation metrics</h2><div>In traditional Neural Networks, you can use accuracy=correct predictions/total predictions, because the models are deterministic. In LLMs, the non-deterministic output and language-based evaluation is difficult.</div><div><ul style="text-align: left;"><li><b>Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scoring:</b> Assesses the quality of automatically generated summaries by comparing them to human-generated reference <b>summaries</b>.</li><li><b>BiLingual Evaluation Understudy (BLEU): </b>Evaluates the quality of machine-translated text, by comparing it to human-generated <b>translations</b>. </li></ul></div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgvBNl2o1YIn7t59tsfBJ0CAbNlwwld3VmzP7K8CZNHWVidJv_fyBqrCR4v1aIH74eI8Pv1uEIDTxYJ8Ij1MKXSdzC-xDKNX470Se2BLCTKnufpgDDQ-2gKx3pPuYlqecW0PEp-DhPlaHH4xLCRXDoVeizqCxXRaDmCQXoQI8vnL1d1bOahg5Phu5JDl7c" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="219" data-original-width="736" height="95" src="https://blogger.googleusercontent.com/img/a/AVvXsEgvBNl2o1YIn7t59tsfBJ0CAbNlwwld3VmzP7K8CZNHWVidJv_fyBqrCR4v1aIH74eI8Pv1uEIDTxYJ8Ij1MKXSdzC-xDKNX470Se2BLCTKnufpgDDQ-2gKx3pPuYlqecW0PEp-DhPlaHH4xLCRXDoVeizqCxXRaDmCQXoQI8vnL1d1bOahg5Phu5JDl7c" width="320" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">"gram" just means "word"</td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><br /></div>This is how the ROUGE score would be calculated for unigrams. Note that the single extra "not" word won't change the ROUGE score.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgJc7CFHvXXPcG3kPhau0TNghx8pW2B4yePrm5kJBfWA0UEki0AZiAxvpHQeStlfPsPz84WBIvP6wbhUU9NK2z35FbCZLbJsqxdLYIr8zGVQgIb6504TIWEXorihQPST4vhQqeOqnp6SbNoBYWkS-06jh9sPWEuibHg6I3m8WZo0l7siT4N6vg3l6Crx-Q" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="333" data-original-width="905" height="237" src="https://blogger.googleusercontent.com/img/a/AVvXsEgJc7CFHvXXPcG3kPhau0TNghx8pW2B4yePrm5kJBfWA0UEki0AZiAxvpHQeStlfPsPz84WBIvP6wbhUU9NK2z35FbCZLbJsqxdLYIr8zGVQgIb6504TIWEXorihQPST4vhQqeOqnp6SbNoBYWkS-06jh9sPWEuibHg6I3m8WZo0l7siT4N6vg3l6Crx-Q=w640-h237" width="640" /></a></div><br />With bigrams, you work with pairs of words, which indicate in a small way, the ordering of the sentence. Scores would be even lower with larger sentences, thereby better capturing the difference in sentences.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgFrZXMIVC4BECjAys1Ap9BiPY4yHsugZ13OrMwY_Yi6NUNdEqQL6-YVS5zPcxhOdFGCFiwsyp5uxDxJu4nT4_m3g8oeSTPW_NpbgBvcwUNnG6KOboCcbQOC-Z4JMDAmFuGGDuJJyA2cMAcFtv8Soo-UvQEx310ncItPbPO2-hq_rd6idT4uQPRUgxgUlg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="352" data-original-width="906" height="248" src="https://blogger.googleusercontent.com/img/a/AVvXsEgFrZXMIVC4BECjAys1Ap9BiPY4yHsugZ13OrMwY_Yi6NUNdEqQL6-YVS5zPcxhOdFGCFiwsyp5uxDxJu4nT4_m3g8oeSTPW_NpbgBvcwUNnG6KOboCcbQOC-Z4JMDAmFuGGDuJJyA2cMAcFtv8Soo-UvQEx310ncItPbPO2-hq_rd6idT4uQPRUgxgUlg=w640-h248" width="640" /></a></div><br />Rather than expand this concept with n-grams, we use the <b>Longest Common Subsequence (LCS)</b>. The three scores (recall, precision, f1) together are the ROUGE-L score. The ROUGE score of a particular task (like summarization) cannot be used to compare with the ROUGE score of another task (like translation).</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhOpf8mNIi9axFc4z8aKX27vWGbyxDkEpd_TA7whzpcSzY9iiIBgj5-WgkW5Mtml7zTRv_6QenLBpDFJ7vGpICip4d6MU9s4QCE5vMv_9pLXbvxM1OtWj-VN7npT89l1gfI0Ae1d7x1l6qeC6Admbuw4zHlW46I_zGWZrDJ0i8l1EKyH5MCBWbW4mlFP68" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="331" data-original-width="906" height="234" src="https://blogger.googleusercontent.com/img/a/AVvXsEhOpf8mNIi9axFc4z8aKX27vWGbyxDkEpd_TA7whzpcSzY9iiIBgj5-WgkW5Mtml7zTRv_6QenLBpDFJ7vGpICip4d6MU9s4QCE5vMv_9pLXbvxM1OtWj-VN7npT89l1gfI0Ae1d7x1l6qeC6Admbuw4zHlW46I_zGWZrDJ0i8l1EKyH5MCBWbW4mlFP68=w640-h234" width="640" /></a></div><br />ROUGE clipping is used when there are repetitive words. So only one of the repeating words is considered. There is of course an issue if all the words of a sentence are re-ordered.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi4HVIz61utRRJFl1rUWjNUbzz1aidOYoWQLQW6vYAT1KkIqvkQTBuj_wmt9Pm2GLBAy7MddjgwdIPfISv4fDiYSZKDOAch3pTv0eGcu6Smyk_Sw_k6RA9qV3e69Zara47BnFJPAFLCcgBS7wqjmtqmx_r8nCaLcTxAzrN7l8H6VFjWiMeLQefp1juL9ws" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="359" data-original-width="923" height="248" src="https://blogger.googleusercontent.com/img/a/AVvXsEi4HVIz61utRRJFl1rUWjNUbzz1aidOYoWQLQW6vYAT1KkIqvkQTBuj_wmt9Pm2GLBAy7MddjgwdIPfISv4fDiYSZKDOAch3pTv0eGcu6Smyk_Sw_k6RA9qV3e69Zara47BnFJPAFLCcgBS7wqjmtqmx_r8nCaLcTxAzrN7l8H6VFjWiMeLQefp1juL9ws=w640-h248" width="640" /></a></div><br />BLEU score is calculated a bit differently, since it's for translation. We average the precision across a range of different n-gram sizes. If you were to calculate this by hand, you would carry out multiple calculations and then average all of the results to find the BLEU score. As we get closer and closer to the original sentence, we get a score that is closer and closer to 1.</div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEh4c5ueSRJtHJ31DSC8frfq09J_KqCXXjFmEH1KTxlSy8AJqkEKLGvKOffyqgyGbKWxfKNIK95yE6M5XwFyCZpfIjRWJ63qSAt2GSDtaIiqP8L0EHcmqVt-Q0A8RavANNXEKosU9J_wVJP5mNf3sWYT3dX9fa0yb9FbzhPKd_BELuQqXtA2XPdEU1HY1zo" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="361" data-original-width="853" height="270" src="https://blogger.googleusercontent.com/img/a/AVvXsEh4c5ueSRJtHJ31DSC8frfq09J_KqCXXjFmEH1KTxlSy8AJqkEKLGvKOffyqgyGbKWxfKNIK95yE6M5XwFyCZpfIjRWJ63qSAt2GSDtaIiqP8L0EHcmqVt-Q0A8RavANNXEKosU9J_wVJP5mNf3sWYT3dX9fa0yb9FbzhPKd_BELuQqXtA2XPdEU1HY1zo=w640-h270" width="640" /></a></div><br /><div>You can use them for simple reference as you iterate over your models, but you shouldn't use them alone to report the final evaluation of a large language model. Use rouge for diagnostic evaluation of</div><div>summarization tasks and BLEU for translation tasks. For overall evaluation of your model's performance, however, you will need to look at one of the evaluation benchmarks like GLUE, SuperGLUE, HELM, MMLU (Massive Multitask Language Understanding), BIG-bench, etc.</div><h2 style="text-align: left;">Benchmarks</h2><div><div><div>Simple evaluation metrics like ROUGE and BLEU scores can only tell you very little about the capabilities of your model. To measure and compare LLMs holistically, you can make use of pre-existing datasets and associated benchmarks established by LLM researchers specifically for this purpose.</div><div>Selecting the right evaluation dataset is vital, to accurately assess LLM performance and understand its true capabilities. It's useful to <b>select datasets that isolate specific model skills</b>, like reasoning or common sense knowledge, and those that focus on potential risks, such as disinformation or copyright infringement.</div><div>An important issue that you should <b>consider: whether the model has seen your evaluation data during training</b>. You'll get a more accurate and useful sense of the model's capabilities by <b>evaluating its performance on data that it hasn't seen before</b>. </div></div><div><ul style="text-align: left;"><li><b>General Language Understanding Evaluation (GLUE):</b> Introduced in 2018. It's a collection of natural language tasks, such as sentiment analysis and question-answering, created to encourage the development of models that can generalize across multiple tasks, You can use it to measure and compare model performance.</li><li><b>SuperGLUE</b> is a successor to GLUE, created in 2019, to address GLUE's limitations. It consists of a series of tasks, some of which are not included in GLUE, and some of which are more challenging versions of the same tasks. Tasks like multi-sentence reasoning, and reading comprehension.</li></ul></div><div><br /></div><div>As models get larger, their performance against benchmarks such as SuperGLUE start to match human ability on specific tasks, but subjectively we can see that they're not performing at human level at tasks in general. </div></div></div><div><ul style="text-align: left;"><li><b>Massive Multitask Language Understanding (MMLU):</b> Released in 2021, it's designed specifically for modern LLMs. To perform well models must possess extensive world knowledge and problem-solving ability. Models are tested on elementary mathematics, US history, computer science, law, and more.</li><li><b>BIG-bench: </b>Introduced in 2022, currently consists of 204 tasks, ranging through linguistics, childhood development, math, common sense reasoning, biology, physics, social bias, software development and more. BIG-bench comes in three different sizes, to keep costs achievable, as running these large benchmarks can incur large inference costs.</li><li><b>Holistic Evaluation of Language Models (HELM):</b> It aims to improve the transparency of models, and to offer guidance on which models perform well for specific tasks. HELM takes a multi-metric approach, measuring seven metrics across 16 core scenarios, ensuring that trade-offs between models and metrics are clearly exposed. One important feature of HELM is that it assesses on metrics beyond basic accuracy measures, like precision of the F1 score. The benchmark also includes metrics for fairness, bias, and toxicity, which are becoming increasingly important to assess as LLMs become more capable of human-like language generation, and in turn of exhibiting potentially harmful behavior. HELM is a living benchmark that aims to continuously evolve with the addition of new scenarios, metrics, and models. </li></ul></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjKlnKo8vIrrCSiRal9MCcn_AO47Zjb8JPSIY25E7BFJ0ZiTiafEWuP-VjPtHVrBf3u2BXHddSWLzxQEu1EBGuyOWq8O5uYdJ6nSC_qk8VJpdTsDdVuHVI4ICvek3bLiL4WO7Af66Nt92E0cn8pokf_IMas9SwGYWiSi0yNv91HlyDvPrWGticZOPx4YlU" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="349" data-original-width="886" height="252" src="https://blogger.googleusercontent.com/img/a/AVvXsEjKlnKo8vIrrCSiRal9MCcn_AO47Zjb8JPSIY25E7BFJ0ZiTiafEWuP-VjPtHVrBf3u2BXHddSWLzxQEu1EBGuyOWq8O5uYdJ6nSC_qk8VJpdTsDdVuHVI4ICvek3bLiL4WO7Af66Nt92E0cn8pokf_IMas9SwGYWiSi0yNv91HlyDvPrWGticZOPx4YlU=w640-h252" width="640" /></a></div><br /><br /></div><h2 style="text-align: left;">Parameter efficient fine-tuning (PEFT)</h2><div><div>Full fine-tuning requires memory not just to store the model, optimizer states, gradients, forward activations, and temporary memory throughout the training process. PEFT only updates a small subset of parameters. Some PEFT techniques freeze most of the model weights and focus on fine tuning a subset of existing model parameters, for example, particular layers or components. Other techniques don't touch the original model weights at all, and instead add a small number of new parameters or layers and fine-tune only the new components. With PEFT, most if not all of the LLM weights are kept frozen. As a result, the number of trained parameters is sometimes just 15-20% of the original LLM weights. So PEFT can often be performed on a single GPU. And because the original LLM is only slightly modified or left unchanged, PEFT is less prone to the catastrophic forgetting problems of full fine-tuning. The new parameters are combined with the original LLM weights for inference. PEFT weights are trained for each task and can be easily swapped out for inference, allowing efficient adaptation of the original model to multiple tasks. </div></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiM6XUjXhI82LGT4vtsvTC6doBSwyXfXLUtM53xVinJg8FBLJKvfca4Jf7KOsLsmEXI_RID5pY7cvm8s79bOLqjG5YWlr6VhvEqU1pCXN4jUcdx912nl5D74cFTCEfPrIU8wZyOJo_b7aLyTzMh7dvxW7wmnqtDWuzs9idd8fbOIEe3WN3-N5wpgwK37Rw/s868/PEFT.gif" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="356" data-original-width="868" height="262" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiM6XUjXhI82LGT4vtsvTC6doBSwyXfXLUtM53xVinJg8FBLJKvfca4Jf7KOsLsmEXI_RID5pY7cvm8s79bOLqjG5YWlr6VhvEqU1pCXN4jUcdx912nl5D74cFTCEfPrIU8wZyOJo_b7aLyTzMh7dvxW7wmnqtDWuzs9idd8fbOIEe3WN3-N5wpgwK37Rw/w640-h262/PEFT.gif" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">New parameters augmented with the original LLM</td></tr></tbody></table><br /><h4 style="text-align: left;">PEFT tradeoffs</h4><div>There are several methods you can use for parameter efficient fine-tuning, each with trade-offs on:</div><div><ul style="text-align: left;"><li>Parameter efficiency</li><li>Memory efficiency </li><li>Model performance</li><li>Inference costs</li><li>Training speed</li></ul></div><h3 style="text-align: left;">PEFT methods</h3><div><ul style="text-align: left;"><li><b>Selective methods:</b> They fine-tune only a subset of the original LLM parameters. You can train only certain components of the model or specific layers, or even individual parameter types.</li><li><b>Reparameterization: </b>These work with the original LLM parameters, but reduce the number of parameters to train by creating new low rank transformations of the original network weights. A commonly used technique of this type is <b>LoRA</b>.</li><li><b>Additive methods:</b> These carry out fine-tuning by keeping all of the original LLM weights frozen and introducing new trainable components. There are two main approaches: <b>Adapter methods </b>add new trainable layers to the architecture of the model, typically inside the encoder or decoder components after the attention or feed-forward layers. <b>Soft prompt methods</b>, keep the model architecture fixed and frozen, and focus on manipulating the input to achieve better performance. This can be done by adding trainable parameters to the prompt embeddings or keeping the input fixed and retraining the embedding weights. </li></ul></div><div><br /></div><div><h3>PEFT: Low Rank Adaptation (LoRA) of LLMs</h3><div>It's a PEFT technique in the re-parametrization category. A transformer typically has weights of dimension <b>512 * 64 = 32768</b>. But with LoRA of rank = 8, matrix A has dimensions <b>8 * 64 = 512</b>, and matrix B has dimensions <b>512 * 8 = 4096</b>. That's 4096 + 512 = 4608 parameters, which is an 86% reduction in parameters to train, and can be done with a single GPU. </div></div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiH-ZRb1_MTVhzvTcTBbimbv4_wDezjKYPj6UWYuswOuZyvERoNDJpzdX9d1RgA8pFvs5tOrOrXZD5uOW-OAJd23cIxxdDHtVnPmCDt3NBIiq7fxCOiD1ov7WLW6O0iOj1HYd_dXmqZzlzvcIzrJz_T4T5a_tX6aSgTkObXz36dJpntRJwUMSIA-K0l2gc" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="346" data-original-width="852" height="260" src="https://blogger.googleusercontent.com/img/a/AVvXsEiH-ZRb1_MTVhzvTcTBbimbv4_wDezjKYPj6UWYuswOuZyvERoNDJpzdX9d1RgA8pFvs5tOrOrXZD5uOW-OAJd23cIxxdDHtVnPmCDt3NBIiq7fxCOiD1ov7WLW6O0iOj1HYd_dXmqZzlzvcIzrJz_T4T5a_tX6aSgTkObXz36dJpntRJwUMSIA-K0l2gc=w640-h260" width="640" /></a></div>To train for a particular task, you'd train the LoRA matrices A and B, and then multiply them and add them to the original frozen weights (depicted by the snowflake). The summed matrix replaces the original weights of the model. In the same way, you can train for a different task and do similar replacement. </div><div>Deciding the best rank for LoRA matrices:</div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjkKBJXb5Yv43noYniY51hxKV2N_74cb4k275zTutFwPkzUgvZlzjojnugyE4WhHR0PQ8sf_0cI-w4lMGGsSF2dieIPXrtCKPhMCXr2zPP6RQlbYn-9N8uN8_1w9_gzL8G9OJqMPYRq27tAIn3Lgr2eqFDIrJxMwFZ25nGLj9b58f8cA8FGKhJE1LxK2OY" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="230" data-original-width="492" height="188" src="https://blogger.googleusercontent.com/img/a/AVvXsEjkKBJXb5Yv43noYniY51hxKV2N_74cb4k275zTutFwPkzUgvZlzjojnugyE4WhHR0PQ8sf_0cI-w4lMGGsSF2dieIPXrtCKPhMCXr2zPP6RQlbYn-9N8uN8_1w9_gzL8G9OJqMPYRq27tAIn3Lgr2eqFDIrJxMwFZ25nGLj9b58f8cA8FGKhJE1LxK2OY=w400-h188" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">LoRA rank, resulting loss values and various metrics</td></tr></tbody></table><br />Researchers observed that the validation loss plateaued after rank sizes of 16. The numbers in bold show the best values obtained for each metric and validation loss. So you could choose a rank ranging from 4 to 16, but beyond 16, there's no benefit. Training all the weights of the model does give better results than LoRA, but given the flexibility and lesser compute of LoRA, the slight loss of performance is forgivable.</div><h3 style="text-align: left;">PEFT: Prompt Tuning with Soft Prompts </h3><div>Prompt tuning is not prompt engineering. The problem with prompt engineering is that it can require a lot of manual effort to write and try different prompts. You're also limited by the length of the context window, and at the end of the day, you may still not achieve the desired performance. </div><div><br /></div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgz7NmM5YXdOslecipMSQgoMeS0ALBwr6X5n4PLAXH3DOVkYT6T_U-ocXXFdjYM5mzUwp0aMQq37cnZiu1CyismoeYqXphwQEzyegBJTIVBbLhzoIbOB-BzVhZQdJ3OWkgw16Hd9c6KctnFDUTZuFHRhXVlOfcfEthE78Soip9FfN-tatLTm_rUSA9DPjo" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="215" data-original-width="744" height="184" src="https://blogger.googleusercontent.com/img/a/AVvXsEgz7NmM5YXdOslecipMSQgoMeS0ALBwr6X5n4PLAXH3DOVkYT6T_U-ocXXFdjYM5mzUwp0aMQq37cnZiu1CyismoeYqXphwQEzyegBJTIVBbLhzoIbOB-BzVhZQdJ3OWkgw16Hd9c6KctnFDUTZuFHRhXVlOfcfEthE78Soip9FfN-tatLTm_rUSA9DPjo=w640-h184" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Pre-pended soft prompts shown in yellow</td></tr></tbody></table><br /><div>With prompt tuning, you add additional trainable tokens to your prompt and leave it up to the supervised learning process to determine their optimal values. The set of trainable tokens is called a soft prompt, and it gets pre-pended to embedding vectors that represent your input text. Approximately 20 and 100 virtual tokens can be sufficient for good performance. Soft prompts can easily be switched with a different set of trained soft prompts, for a different task.</div><div><br /></div>How soft prompts are different from full fine tuning: </div><div><b>1. Embedding space:</b> Tokens typically are in a fixed location in the embedding vector space. However, soft prompts are not fixed discrete words of natural language. Instead, they are virtual tokens that can take on any value within the continuous multidimensional embedding space (however, they do form tight semantic clusters with fixed tokens, meaning the clusters have words with similar meanings). Through supervised learning, the model learns the values for these virtual tokens that maximize performance for a given task. </div><div><b>2. Weight updation:</b> In full fine tuning, the training data set consists of input prompts and</div><div>output completions or labels. The weights of the large language model are updated during supervised learning. In contrast, with prompt tuning, the weights of the large language model are frozen and the underlying model does not get updated. Instead, the embedding vectors of the soft prompt gets updated over time to optimize the model's completion of the prompt.</div><div><br /></div><div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEh_rexQa519CV8FTo_BHFWZvJXqM_tlYGpMOnOlekjI9YkEWWFmJKOjOmWRiIqp375bjS4nkaLEkHKRcBj9UAbZJPlHpAqHsmx6Do00Ej2TN1z5XtJNTVCUuegJUvS_YXUmLjUAJ-JH4FjUYGrQFhiD_d4ua7W6D002kQgoL6ujHQL2KFGPfkilsAA4tP4" style="margin-left: auto; margin-right: auto;"><img alt="" data-original-height="358" data-original-width="783" height="292" src="https://blogger.googleusercontent.com/img/a/AVvXsEh_rexQa519CV8FTo_BHFWZvJXqM_tlYGpMOnOlekjI9YkEWWFmJKOjOmWRiIqp375bjS4nkaLEkHKRcBj9UAbZJPlHpAqHsmx6Do00Ej2TN1z5XtJNTVCUuegJUvS_YXUmLjUAJ-JH4FjUYGrQFhiD_d4ua7W6D002kQgoL6ujHQL2KFGPfkilsAA4tP4=w640-h292" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">For larger models, prompt tuning can be as effective as full fine tuning</td></tr></tbody></table><br /><br /></div><div><br /></div><div>Continued in <a href="https://nrecursions.blogspot.com/2023/07/generative-ai-with-large-language_12.html" target="_blank">Part 3</a>.<br /></div>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-9332348409508874962023-07-06T17:19:00.006+05:302023-07-21T19:23:24.848+05:30Generative AI with Large Language Models: Part 1<p>To learn more about Generative AI, there's <a href="https://www.coursera.org/learn/generative-ai-with-llms" target="_blank">a 3 week course</a> I went through (the free version). Am glad I did, because there's so much that was taught which various blogs and Wikipedia do not inform us about. I'm creating these blog posts as a summary that I can refer later, since going through a blog post is faster than finding information in multiple videos.<br /></p><p><i><b></b></i></p><blockquote><i><b>LICENSE: Images shown in this blog post and following parts of the blog are screenshots taken from the <a href="https://www.coursera.org/learn/generative-ai-with-llms" target="_blank">Generative AI course</a>. You may not use or distribute these or the text content for commercial purposes. It was created by <a href="https://www.deeplearning.ai/">DeepLearning.ai</a>, and is licensed under a <a href="https://creativecommons.org/licenses/by-sa/2.0/legalcode" target="_blank">creative commons license</a>. </b></i></blockquote><p></p><p>Here are some of the main points:</p><p>Generative AI is a subset of Machine Learning, which finds statistical patterns in massive datasets. This is a (not very accurate) representation of how massive the current Large Language Models (LLM's) are:</p><p style="text-align: center;"><img alt="" height="175" 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width="320" /> </p><ul style="text-align: left;"><li><b>Prompt: </b>The text you pass to an LLM.</li><li><b>Context window:</b> The memory available to the prompt.</li><li><b>Completion: </b>The output of the LLM.</li></ul><h3 style="text-align: left;">LLM's can be used for:<br /></h3><ul style="text-align: left;"><li><b>Chatbots.</b></li><li><b>Creative: </b>Writing essays or summarizing conversations.</li><li><b>Translation tasks:</b> between different languages and from natural language to machine code.</li><li><b>Named entity recognition:</b> Retrieving specific information from text.<br /></li></ul><p>The <b>capabilities</b> of LLM's <b>can be augmented</b> by connecting them to external data sources or invoking API's. </p><p style="text-align: center;"><img alt="" height="256" 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" width="640" /></p><h3 style="text-align: left;">Transformers to the rescue</h3><p>In 2017, Transformers arrived, and were capable of capturing context and meaning of language, much better than Recurrent Neural Networks (RNN's) ever could.<br /></p><ul style="text-align: left;"><li><b>Scaling:</b> It can be scaled to use multicore GPU's.</li><li><b>Data parallelism:</b> Can process data in parallel, thus handling much larger data sets.</li><li><b>Attention:</b> Can establish relevance of meaning and position of words in sentences.</li></ul><h3 style="text-align: left;">Self attention</h3><div style="text-align: left;">This an attention map that shows attention weights between each word and every other word. Here, the word "book" is strongly connected with or paying attention to the word "teacher" and the word "student". This is called self-attention and the ability to learn attention in this way across the whole input significantly approves the model's ability to encode language. <br /></div><p style="text-align: center;"><img alt="" height="164" 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width="200" /></p><h3 style="text-align: left;">Overview of Encoder-Decoder</h3><div style="text-align: left;">This section describes a simplistic view of the steps involved in processing words using the Encoder-Decoder model.<br /></div><h3 style="text-align: center;"><img alt="" height="400" 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width="226" /> <br /></h3><p><b>Tokenising:</b> As a first step, words or parts of words are converted into number tokens and fed to the input. Once you use a set of tokens, you have to use the same tokens for decoding.</p><p><b>Word Embedding: </b>Then, token id's are matched to a multi-dimensional vector (called embedding) space, occupying a unique location in the space. The intuition is that these vectors learn to encode the meaning and context of individual tokens in the input sequence (like word2vec did). </p><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="294" 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" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Token id's (342, 879, etc.) mapped to vectors</td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="178" 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style="margin-left: auto; margin-right: auto;" width="320" /></td></tr><tr><td class="tr-caption" style="text-align: center;">A simplistic 3D vector space. Actual vector spaces are much more high dimensional<br /></td></tr></tbody></table><p style="text-align: center;"> </p><p style="text-align: left;">Instead of 512, if the vector was just 3 dimensional, the diagram above shows how the vector space would appear in 3D. The angle and proximity helps the model mathematically "understand" relationships between words, and hence, language.<br /><br /></p><p style="text-align: left;"><b>Positional encoding:</b> Preserves the word order. Relevance of the position of the word in a sentence.</p><p style="text-align: center;"><img alt="" height="294" 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" width="400" /> </p><p style="text-align: left;"><b>Self attention: </b>Applies the self-attention weights to a model (token relationships in the input sequence). This allows the model to "attend" to different parts of the input sequence to better capture the contextual dependencies between words. The transformer architecture has multi-headed self-attention. This means that multiple sets of self-attention weights or heads are learned in parallel independently of each other. The number of attention heads included in the attention layer varies from model to model, but numbers in the range of 12-100 are common. The intuition here is that each self-attention head will learn a different aspect of language. For example, one head may see the relationship between the people entities in our sentence. Whilst another head may focus on the activity of the sentence. Whilst yet another head may focus on some other properties such as if the words rhyme. It's important to note that you don't dictate ahead of time what aspects of language the attention heads will learn. The weights of each head are randomly initialized and given sufficient training data and time, each will automatically learn different aspects of language. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="240" 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style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;"></td></tr></tbody></table><p style="text-align: center;"></p><p><b>Feed-forward network:</b> The output is processed through a fully-connected feed-forward network. The output of this layer is a vector of <a href="https://en.wikipedia.org/wiki/Logit" target="_blank">logits</a> proportional to the probability score for each and every token in the tokenizer dictionary.<br /></p><p style="text-align: center;"><img alt="" height="155" 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width="400" /> <br /></p><p><b>Softmax output:</b> Logits are sent to the softmax layer, where they are normalized into a probability score for each word. This output includes a probability for every word in the vocabulary, so there's likely to be thousands of scores here. One single token will have a score higher than the rest. This is the most likely predicted token. </p><h3 style="text-align: left;">Example: Sequence to sequence prediction</h3><p>If you are translating from French to English, First, the tokenizing is done, the tokens are fed into the Encoder side, passed through the embedding layer, to the multi-headed attention layers, through a feed-forward network to the output of the encoder. At this point, the data that leaves the encoder is a deep representation of the structure and meaning of the input sequence. This representation is inserted into the middle of the decoder to influence the decoder's self-attention mechanisms.<br /></p><p style="text-align: center;"><img alt="" height="281" 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" width="400" /></p><p>Next, a start of sequence token is added to the input of the decoder. This triggers the decoder to predict the next token, which it does based on the contextual understanding that it's being provided from the encoder. The output of the decoder's self-attention layers gets passed through the decoder feed-forward network and through a final softmax output layer. At this point, we have our first token. <br /></p><p><img alt="" height="314" 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fFHwzvZo4LVr/SXbKkKWX68cqauD4vzAc+H5M/8AXQ/4V62WyKb5cX9xP++a63iYS1qQuzFUpLSMjyb/AIW/L/0L8n/fw/4Uf8Lgm/6F+T/v4f8ACvWfLi/55p/3yKNkP/PNP++aXt6H/Pv8Q9nU/mPJv+FwTf8AQvyf9/D/AIUv/C4Jv+hfk/7+H/4mvWNkP/PNP++aNkP/ADzT/vmj29D/AJ9/iPkqfzHk/wDwuCb/AKF+T/v4f/iaP+FwTf8AQvyf9/D/APE16xsh/wCeaf8AfNGyH/nmn/fNHtqH/Pv8Q5Kn8x5P/wALgm/6F+T/AL+H/wCJo/4XBN/0L8n/AH8P/wATXrGyH/nmn/fNGyH/AJ5p/wB80e2of8+/xDkqfzHk3/C4Jv8AoX5P+/h/wpf+FwS/9C/J/wB/D/hXrGyH/nmn/fNGyH/nmn/fNHt6H/Pv8RezqfzHk/8AwuCb/oX5P+/h/wAKT/hcE3/Qvyf9/D/hXrOyH/nmn/fNGyH/AJ5p/wB80e3o/wDPv8Q5Kn8x5N/wt+b/AKF+T/v4f8KP+Fvzf9C/J/38P+Fes7If+eaf980bIf8Anmn/AHzR7ej/AM+/xHyVP5jyb/hb83/Qvyf9/D/hR/wt+b/oX5P+/h/wr1nZD/zzT/vmjZD/AM80/wC+aPb0f+ff4hyVP5jyb/hb83/Qvyf9/D/hR/wt+b/oX5P+/h/wr1nZD/zzT/vmjZD/AM80/wC+aPb0f+ff4hyVP5jyb/hb83/Qvyf9/D/hR/wt+b/oX5P+/h/wr1nZD/zzT/vmjZD/AM80/wC+aPb0P+ff4i9nU/mPJv8Ahb8v/Qvyf9/D/hR/wt+b/oX5P+/h/wAK9Z2Q/wDPNP8AvmjZD/zzT/vmj29D/n3+I+Sp/MeTf8Lfm/6F+T/v4f8ACj/hb83/AEL8n/fw/wCFes7If+eaf980bIf+eaf980e3of8APv8AEOSp/MeTf8Lfm/6F+T/v4f8ACj/hb83/AEL8n/fw/wCFes7If+eaf980bIf+eaf980e3of8APv8AEOSp/MeTf8Lfm/6F+T/v4f8ACj/hb83/AEL8n/fw/wCFes7If+eaf980bIf+eaf980e3of8APv8AEOSp/MeTD4vzf9C/J/38P+FL/wALgl/6F+T/AL+H/CvWNkP/ADzT/vmjZD/zzT/vmj29H/n3+IclT+Y8mb4v3BUhPD77j0/eH/Cq1to/iT4k6xb3WsW72WkRHcFIKgj0UHkk+texBIgchE/75p+4Yx0p/WYx/hwsw9k38TuOhijggjhiULHGoVVHYDgCn5qLf70b/euM2Jc0ZqLf70b/AHoAmzXnHxmP/FJQf9fA/lXoO/3qhq+j6fr1ibPUrcTw53bSSMH1BHNa0Kip1FN9CKkeaLRzGg/EHwvZ+H7C2n1REmigVXXy24IH0rQ/4WV4S/6C6f8Aft/8Kqf8Kw8I/wDQNb/v/J/jSf8ACsPCH/QNb/v/ACf41u3hm76maVVLoXP+FleEv+gsn/ft/wDCj/hZXhL/AKCyf9+3/wAKp/8ACsPCP/QNb/v/ACf40f8ACsPCP/QNb/v/ACf40rYbz/Af73yLn/CyvCX/AEFk/wC/b/4VV1P4n+HItLupLLUUmuliYxR7GG5scdRTf+FYeEf+ga3/AH/k/wAaq6n8L/DbaZdLY2JiuzEwhczOcNjjgmnH6rdbiftbdDwu5uZry5luLiRpJZWLu7HJJNRU6aKW3meCZGjljYq6sMFSOopma+gVraHmu/U9G+EWuT2fiM6Wzk210hO3PRhzmvds14H8JdImu/E/9pbSLe0Q5fsWPAFe7b/evAzDl9toejhr8mot1cpZ2k1zJny4Y2kbHJwBk4qLTtQh1PTbe/t9whnjEibxg4PrVTXX/wCKf1Ln/l1l/wDQDXnmk6da6xqfhm0vQ8tr/YW9od5CudwxuAPOK47aXOg9XzRkV4zavZ3C+GLLV76RLBb2/twJZyquqnCIzZ5HHf0qdLuGznt/s14ToFn4niS2maUmNEMLBlDE/cEjY64oSEz1PVdWtNG02W/vZNkERUMQMnJIUDH1Iq5nnFeMeLJdP1lfHL+dFdW9u2msrB9yIwOGIPToTmn+JBZW2ralcwz2V5aWcMPlwG8a2uLNAgINueVcHr7nIzSKPZM0uapWdytxY286iQCSNXAkGGGRnn3qff70CPHPjLrk76nbaMjFYI0EsgB+8T0zXltemfGPSJ49Xt9XVSbeWMRMw/hYdM/WvMs19Hg+X2MbHl1r+0dx8bvDIssTFZEO5WB5Br6a8F6w+u+E7G+l5lZNsnuw4NfMahpGCIpZ2OFUdSa+lfBOlSaH4SsbKbiYJvkHozckVy5ny8q7m2EvdnTcUcVFv96N/vXincS8UcVFv96N/vQBLxRxUW/3o3+9AEtGai3+9G/3oAlzSZqLf70b/emBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AEuaM1Fv96N/vQBLmjNRb/ejf70AS5ozUW/3o3+9AFTzaPNqh5po800AX/No82qHmmjzTQBf82jzaoeaaPNNAF/zaPNqh5po800AX/No82qHmmjzTQBf82jzaoeaaPNNAF/zaPNqh5po800AX/No82qHmmjzTQBf82jzaoeaaPNNAF/zaPNqh5po800AX/No82qHmmjzTQBf82jzaoeaaPNNAF/zaPNqh5xpfONAF7zaPNqj5xpPNNAF/zaPNqh5xpfONAF7zaPNqj5xpPNNAF/zaPNqh5po800AX/No82qHmmjzTQBf82jzaoeaaPNNAF/zaPNqh5po800AX/No82qHmmjzTQBf86jzaoeaaPNNAF/zaPNqh5po800AX/No8wVQ800eaaAMHxN4C0fxLKblt9renrPCB83+8Ohrm7X4OWaThrrWJ5YgeUjiCE/jk16F5po801vHE1Yx5VLQzdKDd2h2l6dY6NYpZWFusMCdFXufUnuau+bVDzTR5prFtvVmiRedlkRkdQyMMFSMgj0qKOC1ieN47eFGjTy0KoAVX+6PQe1VvNNHmmkBR1Dw7Z32p6bcGOBba0Mxe2MIKSmQAHI6dRnoc1q/ZLI2P2L7JB9kxt8jyxsx6bcYqDzjR5poAkSw06OCSCOxtkhkUI8axKFZR0BGORyeKY+laVI0DPptmzW6hYSYFPlgdAvHA+lJ5po800AX/No82qHmmjzTQBNf2trqdnJaXsKTQSDDI4yDXnV98HrCW4Z7LVJ7eIn/VyRiTH0ORXf+aaPNNa061Sn8DsRKnGW6Oa8N/DrR/D9wt27Pe3a8pJMAAnuF9feu082qHmmjzTUznKbvJ3KjFRVkX/Oo82qHmmjzTUWGX/No82qHmmjzTRYC/5tHm1Q800eaaLAX/No82qHmmjzTRYC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgC/wCbR5tUPNNHmmgD/9k=" width="640" /></p><p>You'll continue this loop, passing the output token back to the input to trigger the generation of the next token, until the model predicts an end-of-sequence token. At this point, the final sequence of tokens can be detokenized into words, and you have your output.</p><p><img alt="" height="307" 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width="640" /> <br /></p><ul style="text-align: left;"><li><b>Encoder: </b>Encodes prompts with contextual understanding and produces one vector per input token.</li><li><b>Decoder:</b> Accepts input tokens and generates new tokens.</li></ul><h3 style="text-align: left;">Types of models</h3><h3 style="text-align: center;"><img alt="" height="320" 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width="640" /> <br /></h3><ul style="text-align: left;"><li style="text-align: left;"><b>Encoder only: </b>Normally used for sequence-to-sequence tasks where input and output sequences are of same length. With some modification, it can perform classification and sentiment analysis. Example: <a href="https://en.wikipedia.org/wiki/BERT_(language_model)" target="_blank">BERT</a>.</li><li style="text-align: left;"><b>Encoder-decoder:</b> Sequence to sequence, where input and output are of different length. Can be scaled to perform general text generation. Eg: <a href="https://huggingface.co/facebook/bart-large" target="_blank">BART</a> and <a href="https://huggingface.co/docs/transformers/model_doc/flan-t5" target="_blank">FLAN-T5</a>.</li><li style="text-align: left;"><b>Decoder only:</b> GPT family, <a href="https://huggingface.co/bigscience/bloom" target="_blank">BLOOM</a>, <a href="https://www.ai21.com/blog/announcing-ai21-studio-and-jurassic-1" target="_blank">Jurassic</a>, <a href="https://huggingface.co/docs/transformers/main/model_doc/llama" target="_blank">LLaMa</a> and more.</li></ul><h3 style="text-align: left;">Transformer summary <br /></h3><p>The Attention is all you need paper is <a href="https://arxiv.org/abs/1706.03762" target="_blank">here</a> and published here. It replaces RNNs and convolutional neural networks (CNNs) with an entirely attention-based mechanism. </p><ul style="text-align: left;"><li><b>Multi-head self-attention: </b>Allows the model to attend to different parts of the input sequence.</li><li><b>Feed-forward network:</b> Applies a point-wise fully connected layer to each position separately and identically. </li><li><b>Positional encoding:</b> Encodes the position of each token in
the input sequence, enabling the model to capture the order of the
sequence without the need for recurrent or convolutional operations. </li></ul><p>The Transformer model also uses <b>residual connections</b> and <b>layer normalization</b> to facilitate training and prevent overfitting. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" 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/></td></tr><tr><td class="tr-caption" style="text-align: center;">Details of the Encoder Decoder Transformer<br /></td></tr></tbody></table><p style="text-align: center;"></p><h2 style="text-align: left;">Prompting and prompt engineering</h2><ul style="text-align: left;"><li><b>Zero shot inference:</b> Including your input data within the prompt (large models can handle this easily).</li><li><b>One shot or few-shot inference:</b> Providing a single example to include multiple examples (smaller models need it, and going beyond 5 or 6 exemplars does not yield better results). </li></ul><h3 style="text-align: left;">Configuring model output parameters</h3><p>These parameters are invoked at inference time and control things like the maximum number of tokens in the completion, and how creative the output is. </p><p><b>Max tokens: </b>Specifies that the model can generate x number of tokens or less (until the model predicts an end of sequence token).</p><p>Most large language models by default will operate with <b>greedy decoding</b>, where the model will always choose the word with the highest probability. It works very well for short generation but is susceptible to repeated words or repeated sequences of words. </p><p><b>Random sampling</b> is the easiest way to introduce some variability. The model chooses an output word at random using the probability distribution to weight the selection. </p><p><b>Top k:</b> You specify the number of tokens to randomly choose from. It can help the model have some randomness while preventing the selection of highly improbable completion words. This in turn makes your text generation more likely to sound reasonable and to make sense. <br /></p><p style="text-align: center;"><img alt="" height="119" 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width="400" /> </p><p style="text-align: left;"><b>Top p:</b> Limits the random sampling to the predictions whose combined probabilities do not exceed p. <br /></p><p style="text-align: center;"><img alt="" height="125" 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width="400" /> </p><p style="text-align: left;"><b>Temperature: </b>It influences the shape of the probability distribution that the model calculates for the next token. The higher the temperature, the higher the randomness. The value is a scaling factor that's applied within the final softmax layer of the model that impacts the shape of the probability distribution of the next token. <b>In contrast to the top k and top p parameters, changing the temperature actually alters the predictions that the model will make</b>. If you choose a low value of temperature, say less than one, the resulting probability distribution from the softmax layer is more strongly peaked with the probability being concentrated in a smaller number of words. The model will select from this distribution using random sampling.<br /></p><p style="text-align: left;"><img alt="" height="264" 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width="640" /></p><p style="text-align: left;"><b>Pre-training:</b> A deep statistical representation of language is developed in the pre-training phase when the model learns from gigabytes, terabytes, and even petabytes of unstructured textual data. In this self-supervised learning step, the model internalizes the patterns and structures present in the language. These patterns then enable the model to complete its training objective, which depends on the architecture of the model. Model weights get updated to minimize the loss of the training objective. Pre-training also requires a large amount of compute and the use of GPUs. Data needs processing to increase quality, address bias, and remove other harmful content. As a result of this data quality curation, often only 1-3% of tokens are used for pre-training. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="277" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">LLM pre-training at a high level<br /></td></tr></tbody></table><p style="text-align: center;"></p><p><b>Encoder only model pre-training:</b> Also known as <b>Autoencoding models</b>, and they are pre-trained using masked language modeling (<b>MLM</b>). Tokens in the input sequence are randomly masked, and the training objective is to predict the masked tokens, to reconstruct the original sentence (called a <b>denoising objective</b>). Autoencoding models build bi-directional representations of the input sequence (model has an understanding of the full context of a token and not just of the words that come before). Encoder-only models are suited to tasks that benefit from this bi-directional contexts. Used for sentence classification tasks, sentiment analysis, token-level tasks like named entity recognition or word classification. Eg: BERT and RoBERTa. <br /></p><p><img alt="" height="273" 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width="640" /> </p><p><b>Decoder-only models pre-training:</b> Called <b>autoregressive</b> models, pre-trained using causal language modeling (<b>CLM</b>). Training objective is to predict the next token (called full language modelling) based on the previous sequence of tokens. Most of the input sequence is masked and the model can only see the input tokens leading up to the token in question. The model has no knowledge of the end of the sentence. The model then iterates over the input sequence one by one to predict the next token. In contrast to the encoder architecture, this means that the context is unidirectional. By learning to predict the next token from a vast number of examples, the model builds up a statistical representation of language. Used for text generation, and larger models show strong zero-shot inference abilities, and can often perform a range of tasks well. Eg: GPT and BLOOM.</p><p><img alt="" height="273" 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width="640" /> <br /></p><p><b>Sequence to sequence model pre-training:</b> This model uses both the encoder and decoder parts. The exact details of the pre-training objective vary from model to model. T5, pre-trains the encoder using <b>span corruption</b>, which masks random sequences of input tokens. Those mass sequences are then replaced with a unique Sentinel token (special tokens added to the vocabulary, but do not correspond to any actual word from the input text). The decoder is then tasked with reconstructing the masked token sequences auto-regressively. The output is the Sentinel token followed by the predicted tokens. You can use sequence-to-sequence models for translation, summarization, and question-answering. Another well-known encoder-decoder model is BART. </p><p><img alt="" height="281" 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width="640" /> <br /></p><h2 style="text-align: left;">Computational challenges<br /></h2><p>Running out of memory is a common issue. Here's <a href="https://huggingface.co/docs/transformers/perf_train_gpu_one" target="_blank">the math</a>:</p><ul style="text-align: left;"><li>1 parameter (weight) = 4 bytes (32 bit float)</li><li>1 billion parameters need 4*10^9 bytes = 4GB of GPU RAM needed at 32 bit full precision, just for the weights.</li></ul><p>Training also requires:</p><ul style="text-align: left;"><li>2 Adam optimizer states = 8 bytes per weight.</li><li>Gradients = 4 bytes per weight.</li><li>Activations and temp memory = 8 bytes per weight (worst case). </li></ul><p><b>Total:</b> Approx. 24 bytes per weight. So approx. 80GB RAM for 1 billion weights.<br /></p><h3 style="text-align: left;">Quantization<br /></h3><p>Reduces the memory required. Instead of FP32, FP16 (16 bit float) is used. 8 bit can also be used. Quantization speeds up calculations. BF16 is a popular format, but it isn't well suited for integer calculations.<br /></p><p><img alt="" height="262" 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" width="400" /> </p><p> At 8 bit, a 1 billion weight model would need 20GB GPU RAM. Today's models are at 175 to 500 billion weights in size.</p><h2 style="text-align: left;">Multi-GPU compute strategies</h2><p>PyTorch's <b>Distributed Data Parallel (DDP) </b>method requires model parameters, gradients and optimizer states to fit onto a single GPU.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="246" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">DDP</td></tr></tbody></table><p></p><p>If the model is too big for DDP, use Model Sharding via Fully Sharded Data Parallel (FSDP). The ZeRO technique is used for sharding.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="214" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">FSDP</td></tr></tbody></table><p style="text-align: center;"></p><h2 style="text-align: left;">Scaling laws and compute-optimal models <br /></h2><p style="text-align: center;"><img alt="" height="167" 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" width="400" /></p><p><b>One petaflop per second-day:</b> Is 1,000,000,000,000,000 (one quadrillion) floating point operations per second for one day.</p><p>It will take 8 NVIDIA V100 GPU's to run 1 petaflop/s-day.</p><p>It will take 2 NVIDIA A100 GPU's to run 1 petaflop/s-day.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="203" 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" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Number of petaflops/s-days to pre-train various LLMs (y axis is logarithmic)<br /></td></tr></tbody></table><p style="text-align: left;"> There have been some interesting <a href="https://arxiv.org/abs/2001.08361" target="_blank">papers</a> that examined <a href="https://en.wikipedia.org/wiki/Neural_scaling_law" target="_blank">how Neural Networks scale</a>. The Chinchilla paper is particularly famous, because it pointed out that LLMs may have more parameters (over-parameterised) than required to learn a language, and are under-trained, so they would benefit from more training data.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="175" 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" style="margin-left: auto; margin-right: auto;" width="400" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Optimal data size is 20 times the number of weights<br /></td></tr></tbody></table><p style="text-align: center;"> </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="313" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Smaller models that are better trained, considering Chinchilla laws, can perform better<br /></td></tr></tbody></table><p style="text-align: left;"> </p><h2 style="text-align: left;">Pre-training for domain adaptation</h2><p>For example, training models to summarize legal text, you may need to consider phrases like "mens rea" or "res judicata" or even normal words that mean something entirely different in a legal context. Such words, even in the medical domain, may not occur frequently in training datasets from information scraped from the web or certain general datasets. A doctor may write "1 tab po qid pc & hs", which a pharmacist can understand as "1 tablet by mouth 4 times a day after meals and at bedtime". ChatGPT could explain it, but it demonstrates the varied types of information that an LLM can encounter.</p><p>The <a href="https://arxiv.org/abs/2303.17564" target="_blank">BloombergGPT model</a> for finance is one such LLM that was fine-tuned with fewer parameters than the recommended Chinchilla law. Also, there is limited financial data, so the model could be trained only on a limited set of data.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="279" 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" style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Kaplan and Chinchilla scaling laws compared for BloombergGPT and other LLMs<br /></td></tr></tbody></table><p style="text-align: center;"></p><p><br /></p><p>The BloombergGPT project is a good illustration of pre-training a model for increased domain-specificity, and the challenges that may force trade-offs against compute-optimal model and training configurations.</p><p> </p><p>This series is continued in <a href="https://nrecursions.blogspot.com/2023/07/generative-ai-with-large-language_9.html" target="_blank">Part 2</a>.<br /></p><p style="text-align: center;"></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-6524600269559192982023-07-01T23:09:00.001+05:302023-07-01T23:09:00.146+05:30PySimpleGUI use image as button and toggle it too<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgkRdsj_vSgHrh1KmR9OZ-Uk2s29lRhPd8aqNXgNLFpiU2b_DEpbSuEh4cV2Sxj3aCOzPlVwV-s2ZcI_4wlOEHAapPzG-dBopLvvwxIsbjAPohQlfje9r0lwZOar_4JU0V-JSwp-DlGaxbF2WiNZp1gIQlCwqGJwfWhF6cI3xcRyK_94f3eBY2377cW/s90/pySimpleGUI_imageButtonToggle.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="90" data-original-width="67" height="90" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgkRdsj_vSgHrh1KmR9OZ-Uk2s29lRhPd8aqNXgNLFpiU2b_DEpbSuEh4cV2Sxj3aCOzPlVwV-s2ZcI_4wlOEHAapPzG-dBopLvvwxIsbjAPohQlfje9r0lwZOar_4JU0V-JSwp-DlGaxbF2WiNZp1gIQlCwqGJwfWhF6cI3xcRyK_94f3eBY2377cW/s1600/pySimpleGUI_imageButtonToggle.png" width="67" /></a></div><p></p><p>When I wanted to use an image as a button in PySimpleGUI, I was led to an <a href="https://www.blog.pythonlibrary.org/2021/09/05/pysimplegui-using-an-image-as-a-button/" target="_blank">example</a> that used a massive image and linked to code somewhere else which wrote base64 encoded strings into a file. It didn't have to be this difficult, so I simplified it and also added code to toggle the button images.</p><p>You can use these images as the play.png and pause.png. I created them with Inkscape.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEigpWs0uNqzOxLtstPgoCBifSZJAm719RU572HyfLW-nUMBlRJv9dF8RfE6q1BTZoc5HS6TQoPwXB9aCKly0vC_01u2MMbaJTm9jao1u152kNUukb0V7FN0Xqh9P588gjFd9HtMaY8xAfahW56LPPPAkQLA9qgp1fCyrI2UIIxjQNHrcMYlyY8IReXJ/s35/play.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="35" data-original-width="35" height="35" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEigpWs0uNqzOxLtstPgoCBifSZJAm719RU572HyfLW-nUMBlRJv9dF8RfE6q1BTZoc5HS6TQoPwXB9aCKly0vC_01u2MMbaJTm9jao1u152kNUukb0V7FN0Xqh9P588gjFd9HtMaY8xAfahW56LPPPAkQLA9qgp1fCyrI2UIIxjQNHrcMYlyY8IReXJ/s1600/play.png" width="35" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLhgZ8labbL6Dx1x_NNfjbM0wdsiUnT_c_TT2oKs0uZsJ7fTZNtMgvyNWv8q5OY81TIziE91cu0S7ynWeO8QuG3crKqPlFfz_QT8cdKY2Fm7XE5G6MlwrBHdHN4-9z31oP6xCsv6d5N-GRPK1KFTJZ3RVGv2JgjbGhjXXLEn8mR9hiTR3sot-SVoAc/s35/pause.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="35" data-original-width="35" height="35" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLhgZ8labbL6Dx1x_NNfjbM0wdsiUnT_c_TT2oKs0uZsJ7fTZNtMgvyNWv8q5OY81TIziE91cu0S7ynWeO8QuG3crKqPlFfz_QT8cdKY2Fm7XE5G6MlwrBHdHN4-9z31oP6xCsv6d5N-GRPK1KFTJZ3RVGv2JgjbGhjXXLEn8mR9hiTR3sot-SVoAc/s1600/pause.png" width="35" /></a></div><h3 style="text-align: left;">The code</h3><p><b><i>import base64<br />import PySimpleGUI as simpleGUI<br /> <br />if __name__ == '__main__':<br /> themeName = 'Dark'<br /> simpleGUI.theme(themeName) <br /> #Note: background color is what helps the button's transparent portion seem invisible<br /> backgroundColorOfGUI = simpleGUI.LOOK_AND_FEEL_TABLE[themeName]['BACKGROUND']<br /> hoverColor = 'grey'<br /><br /> PLAYPAUSE = '-playpause-'<br /> PLAY_ICON = 'play.png'<br /> PAUSE_ICON = 'pause.png'<br /> buttonText = ''<br /> icons = dict()<br /> WINDOW_WAIT_TIMEOUT_MILLISECOND = 100<br /> <br /> #---images need to be converted to base64 strings to be used as buttons<br /> imagesToLoad = [PLAY_ICON, PAUSE_ICON]<br /> for image in imagesToLoad:<br /> contents = open(image, 'rb').read()<br /> encodedString = base64.b64encode(contents) <br /> icons[image] = encodedString<br /><br /> basicLayout = [ <br /> [simpleGUI.Button(buttonText, key = PLAYPAUSE, <br /> image_data = icons[PAUSE_ICON], <br /> button_color = (hoverColor, backgroundColorOfGUI), <br /> border_width = 0, <br /> metadata = False)], <br /> ]<br /> windowTitle = 'Toggle Button'<br /> window = simpleGUI.Window(windowTitle, layout = basicLayout)<br /> while True: <br /> event, values = window.read(timeout = WINDOW_WAIT_TIMEOUT_MILLISECOND) <br /> #---toggle button image on click<br /> if event == PLAYPAUSE:<br /> element = window[event]<br /> if element.metadata:<br /> element.update(image_data=icons[PAUSE_ICON]) <br /> else:<br /> element.update(image_data=icons[PLAY_ICON]) <br /> element.metadata = not element.metadata <br /> #---detect close window event<br /> if event == simpleGUI.WIN_CLOSED or event == simpleGUI.Exit:<br /> window.close() <br /> break<br /> print("Program ended") </i><br /></b> <br /> <br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-18859977381307506912023-07-01T16:11:00.012+05:302023-07-03T18:11:26.370+05:30Prompt Engineering Tips<p>I found this <a href="https://www.promptingguide.ai/introduction" target="_blank">interesting guide</a> and corresponding GitHub <a href="https://github.com/dair-ai/Prompt-Engineering-Guide" target="_blank">page</a> for Prompt Engineering. Here's a summary of it:</p><p>Large Language Models (LLM's) can have some parameters to control the output:</p><ul style="text-align: left;"><li><b>Temperature:</b> High value gives more creativity. Low value gives more factual answers.</li><li><b>Top p:</b> A sampling method with temperature. High and low values results are similar to temperature.</li></ul><p>Select either temperature or top-p. Not both.</p><p>Good prompts can (but don't always have to) have an <b>instruction</b>, <b>context</b>, <b>input</b> and <b>output type</b>.</p><br /><h3 style="text-align: left;">Without verbosity<br /></h3><p><span style="font-family: courier;"><b></b></span></p><blockquote><b>What are some activation functions used in Neural Networks. <span style="color: #04ff00;">No explanation. Direct answer</span>.</b></blockquote><p></p><h3 style="text-align: left;">Completion tasks. Zero-shot and few-shot prompting<br /></h3><p>For the LLM to complete a sentence, give it context about what you want it to do: <br /></p><p>Rather than use "<span style="font-family: courier;"><b>The grass is</b></span>". Try this:<br /></p><p><b><span style="font-family: courier;"></span></b></p><blockquote><p><b><span style="color: #04ff00; font-family: courier;">Complete the sentence:</span></b></p><p><b><span style="font-family: courier;">The grass is</span></b></p></blockquote><p><b><span style="font-family: courier;"></span></b></p><p>You can even use comment slashes as prompt hints:<span style="font-family: courier;"><b></b></span></p><blockquote><b><span style="font-family: courier;">This is awesome! <span style="color: #04ff00;">// Positive</span><br />This is bad! <span style="color: #04ff00;">// Negative</span><br />Wow that movie was rad! <span style="color: #04ff00;">// Positive</span><br />What a horrible show! <span style="color: #04ff00;">//</span></span></b></blockquote><p></p><p>The LLM will output "<span style="font-family: courier;"><b>Negative</b></span>".<br /></p><h3 style="text-align: left;">Use markers like ### if necessary<br /></h3><p><span style="font-family: courier;"><b></b></span></p><blockquote><b><span style="color: #04ff00; font-family: courier;">###</span><span style="font-family: courier;"> Instruction <span style="color: #04ff00;">###</span><br />Translate the text below to French:<br />Text: "How are you"</span></b></blockquote><h3 style="text-align: left;"> Be specific to extract text<br /></h3><p><span style="font-family: courier;"><b></b></span></p><blockquote><b><span style="font-family: courier;">Extract the names of vegetables and fruits in the following text:<br />Format as: <br /><span style="color: #04ff00;"><vegetables>: <list of comma separated names of vegetables><br /><fruits>: <list of comma separated names of fruits></span><br />Input: "Ravi spent ten dollars on apples, fifty dollars on mangoes. Jyothi decided to buy some tomatoes, onions, mulberries, pineapple and drumstick. Tinku bought some bread and egg puffs."</span></b></blockquote> <p></p><p><span style="font-family: courier;"></span></p><blockquote><p><b><span style="font-family: courier;">Instruction: Classify these names into ethnicity <span style="color: #38761d;">like </span><span style="color: #04ff00;">European, Asian or African</span>:<br />Text: "Wong Fei Hung visited Europe to meet John Durham during the Summer when Abidemi Bamidele and Karan Sinha were planning to meet them in America"<br /><span style="color: #04ff00;">Ethnicity:</span> <br /></span></b></p><p></p><p> </p></blockquote><p></p><h3 style="text-align: left;">Role prompting</h3><p><span style="font-family: courier;"><b></b></span></p><blockquote><b><span style="font-family: courier;">This is a conversation of a person trying to explain smartphones in a way that their grandmother can understand it. The grandmother does not know technical terms, so the answers should be simple and should explain it with tasks and items that the grandmother is familiar with:<br /><span style="color: #04ff00;">Grandmother:</span> Explain how the phone is charged.<br /><span style="color: #04ff00;">Helper:</span> Similar to how you use a battery to run a clock, the smartphone has a battery which you don't have to replace when it runs out of charge. You can connect it to an electrical socket, and the electricity will get stored in the battery again. Like re-filling a bucket with water.<br /><span style="color: #04ff00;">Grandmother:</span> Explain how the smartphone knows which part of the screen you touched it at.<br /><span style="color: #04ff00;">Helper:</span></span></b></blockquote><b></b><p>It can be simpler:</p><p><span style="font-family: courier;"><b></b></span></p><blockquote><b style="font-family: courier;"><span style="color: #04ff00;">You are an interviewer</span>. Ask me ten questions one by one and give me feedback about each of my answers. The interview is for the role of a fullstack developer. Technologies used: Python, AWS, Django, Redis, PostgreSQL, MongoDB, Kubernetes, Docker, NGINX.</b></blockquote><br /><p></p><h3 style="text-align: left;">Code generation</h3><p><span style="font-family: courier;"><b></b></span></p><blockquote><b style="font-family: courier;">I have two database tables db1 and db2. The db1 table has columns id, recipe_ingredient_name and quantity. The db2 table has columns id, recipe_name, ingredients_required and recipe text. <span style="color: #04ff00;">Write an SQL query</span> to join data from both tables based on id, where recipe ingredients match.</b></blockquote> <br /><p></p><h3 style="text-align: left;">Chain of Thought (CoT) prompting</h3><p>This is one of the most interesting examples of CoT I created.</p><p><span style="font-family: courier;"><b></b></span></p><blockquote><b style="font-family: courier;"><span style="color: #04ff00;">You are driving a bus</span> containing 50 people. At the first stop, a man named Jack gets off the bus. Now there are 49 people in the bus. At the second stop, 5 people named Tim, Brad, Janet, Julia and Yennifer enter the bus, and 6 people named Obama, Bush, Clinton, Bill, Jeff, Elon get out of the bus. What is the name of the bus driver?</b></blockquote><p></p><p>ChatGPT couldn't figure it out.When I mentioned "<span style="font-family: courier;"><b>The question mentions that you are the bus driver</b></span>", it responded with "<span style="font-family: courier;"><b>Apologies for misunderstanding your question. In that case, as the bus driver in this scenario, my name would be AI since I am an AI language model assisting you in this conversation</b></span>".</p><p>It can also be given a hint like this (which is more useful in arithmetic scenarios):</p><p><span style="font-family: courier;"><b></b></span></p><blockquote><b><span style="font-family: courier;">You are driving a bus containing 50 people. At the first stop, a man named Jack gets off the bus. Now there are 49 people in the bus. At the second stop, 5 people named Tim, Brad, Janet, Julia and Yennifer enter the bus, and 6 people named Obama, Bush, Clinton, Bill, Jeff, Elon get out of the bus. What is the name of the bus driver?<br /><span style="color: #04ff00;">Hint: Work step by step to determine the name of the bus driver even if the name is not explicitly mentioned.</span></span> </b></blockquote><p></p><p>But this works better: <br /></p><p><span style="font-family: courier;"></span></p><blockquote><b><span style="font-family: courier;">You are driving a bus containing 50 people. At the first stop, a man named Jack gets off the bus. Now there are 49 people in the bus. At the second stop, 5 people named Tim, Brad, Janet, Julia and Yennifer enter the bus, and 6 people named Obama, Bush, Clinton, Bill, Jeff, Elon get out of the bus. What is the name of the bus driver?<br /><span style="color: #04ff00;">Hint: ignore the number of passengers and consider who drives the bus even if the name is not mentioned</span></span></b></blockquote><p></p><h2 style="text-align: left;">There are more prompting techniques: </h2><ul style="text-align: left;"><li><a href="https://www.promptingguide.ai/techniques/cot#automatic-chain-of-thought-auto-cot" target="_blank">Auto-CoT</a>: Clusters various types of questions, selects the appropriate cluster and generates reasoning using zero-shot technique.<br /></li><li><a href="https://www.promptingguide.ai/techniques/consistency" target="_blank">Self consistency</a>:Generates multiple outputs and selects the most consistent one.<br /></li><li><a href="https://www.promptingguide.ai/techniques/knowledge" target="_blank">Generate knowledge</a>: Generate nuggets of knowledge which are then used for reasoning.<br /></li><li><a href="https://www.promptingguide.ai/techniques/tot" target="_blank">Tree of thought</a>: Explores various paths of reasoning.<br /></li><li><a href="https://www.promptingguide.ai/techniques/rag" target="_blank">Retrieval augmented generation</a> (RAG): Access and incorporate external knowledge to improve reasoning.<br /></li><li><a href="https://www.promptingguide.ai/techniques/art" target="_blank">Automatic reasoning and tool use</a> (ART): Stops the reasoning process in-between to incorporate new knowledge and then continue reasoning.<br /></li><li><a href="https://www.promptingguide.ai/techniques/ape" target="_blank">Auto-prompt engineer</a> (APE): Automatically generates prompts that are better than what we generate.</li><li><a href="https://www.promptingguide.ai/techniques/activeprompt" target="_blank">Active prompt</a>: Questions are selected for annotating various task-specific prompts, and the most uncertain ones are sent to humans for annotation. </li><li><a href="https://www.promptingguide.ai/techniques/dsp" target="_blank">Directional stimulus prompting</a>: Performing tasks using a hint which provides a direction of approach.</li><li><a href="https://www.promptingguide.ai/techniques/react" target="_blank">ReAct</a>: Interleaves Reasoning and Act (performing a task) multiple times to find an answer.</li><li><a href="https://www.promptingguide.ai/techniques/multimodalcot" target="_blank">Multimodal CoT</a>: Incorporates computer vision and text to get an answer.</li><li><a href="https://www.promptingguide.ai/techniques/graph" target="_blank">Graph prompting</a>: Tasks find the most relevant knowledge from the pre-train model in a task-specific manner.<br /></li></ul><h1 style="text-align: left;">Text to image prompting<br /></h1><p></p><p>This is a field where one needs a lot of knowledge of how to prompt and also how to modify the image after it is generated. I found <a href="https://github.com/imJunaidAfzal/Prompt-Engineering" target="_blank">this</a> repository, <a href="https://openart.ai/presets" target="_blank">these</a> prompt templates and <a href="https://cdn.openart.ai/assets/Stable%20Diffusion%20Prompt%20Book%20From%20OpenArt%2011-13.pdf" target="_blank">this</a> pdf to be helpful. You can try StableDiffusionOnline's <a href="https://stablediffusionweb.com/#demo" target="_blank">playground</a> too. To modify an image after generation, you can use <a href="https://huggingface.co/spaces/fffiloni/stable-diffusion-img2img" target="_blank">img2img</a>.</p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-44790132719838480972023-06-25T16:29:00.009+05:302023-07-01T22:10:55.834+05:30The creation of GrootGPT <p>When ChatGPT was first released, I tried creating a basic Android app using ChatGPT. When a developer created <a href="https://www.youtube.com/watch?v=xIeP0TkvvoM" target="_blank">Flappy Bird using ChatGPT</a>, I considered creating a web-page the same way. Building a static web page would be boring, so on 28th May 2023, I thought it'd be fun to make an interactive page that replies like Groot. Turns out, I'm not the only one who had this idea. </p><ul style="text-align: left;"><li>A person made <a href="https://www.reddit.com/r/ChatGPT/comments/ztgsfe/i_am_groot/" target="_blank">ChatGPT reply like Groot</a>. </li><li>There's an <a href="https://techcrunch.com/2014/09/15/i-am-groot/" target="_blank">SMS bot like Groot</a>.</li><li>There's also a <a href="https://grootgpt.siwei.io/" target="_blank">GrootGPT</a> which is code adapted from <a href="https://cat-gpt.com/" target="_blank">CatGPT</a>.</li></ul><p>Anyway, this is the <a href="https://nav9.github.io/grootgpt/" target="_blank">GrootGPT which I built</a> without typing any code. ChatGPT created and refined the code based on prompts I gave it. <b>GrootGPT is unique, with the ability to output emoticons to user prompts that reflect specific emotions</b>:<br /></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRlqJYI16bSgn1u_CR1IYrkVVONNaw7MJb_xfvfkEDTfFZIina_k19c9Yd1e9LlDKrWshVYBvob48FXHtk_3_HcCZKfeIo5BeHx4QIxBYtdmdtYJrdU-bOrtn0JG-upS2GxHXBLqG-OoAkBZWCsayCNlOEdb_1QV6G1NfWkavznt9ifBsFkeaP8oGUi18/s613/GrootGPT.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="498" data-original-width="613" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjRlqJYI16bSgn1u_CR1IYrkVVONNaw7MJb_xfvfkEDTfFZIina_k19c9Yd1e9LlDKrWshVYBvob48FXHtk_3_HcCZKfeIo5BeHx4QIxBYtdmdtYJrdU-bOrtn0JG-upS2GxHXBLqG-OoAkBZWCsayCNlOEdb_1QV6G1NfWkavznt9ifBsFkeaP8oGUi18/s16000/GrootGPT.png" /></a></div><br /><p></p><h2 style="text-align: left;">Prompt Engineering</h2><p>First, it helps to prime ChatGPT about what you want it to do. There's a skill people need to have, to perform Prompt Engineering. There's a <a href="https://github.com/dair-ai/Prompt-Engineering-Guide" target="_blank">comprehensive guide here</a> and a nice summary <a href="https://blog.finxter.com/wp-content/uploads/2023/03/Finxter_Prompting_OpenAI-2.pdf" target="_blank">here</a>.<br /></p><p>I found this prompting guide too on the internet. <br /></p><ul style="text-align: left;"><li><b>Tone:</b> Specify the desired tone (e.g., formal, casual, informative, persuasive).</li><li><b>Format: </b>Define the format or structure (e.g., essay, bullet points, outline).</li><li><b>Act as:</b> Indicate a role or perspective to adopt (e.g., expert, critic, enthusiast).</li><li><b>Objective: </b>State the goal or purpose of the response (e.g., inform, persuade).</li><li><b>Context: </b>Provide background information, data, or context for content generation.</li><li><b>Scope:</b> Define the scope or range of the topic.</li><li><b>Keywords:</b> List important keywords or phrases to be included.</li><li><b>Limitations: </b>Specify constraints, such as word or character count.</li><li><b>Examples:</b> Provide examples of desired style, structure, or content.</li><li><b>Deadline:</b> Mention deadlines or time frames for time-sensitive responses.</li><li><b>Audience: </b>Specify the target audience for tailored content.</li><li><b>Language:</b> Indicate the language for the response, if different from the prompt.</li><li><b>Citations:</b> Request the inclusion of citations or sources to support information.</li><li><b>Points of view:</b> Ask AI to consider multiple perspectives or opinions.</li><li><b>Counterarguments:</b> Request addressing potential counterarguments.</li><li><b>Terminology: </b>Specify industry-specific or technical terms to use or avoid.</li><li><b>Analogies:</b> Ask AI to use analogies or examples to clarify concepts.</li><li><b>Quotes:</b> Request inclusion of relevant quotes or statements from experts.</li><li><b>Statistics:</b> Encourage the use of statistics or data to support claims.</li><li><b>Call to action: </b>Request a clear call to action or next steps.</li><li><b>Sensitivity:</b> Mention sensitive topics or issues to be handled with care or avoided.</li></ul><p>The bullet points above were actually in an image. I used <a href="https://github.com/tesseract-ocr/tesseract" target="_blank">Tesseract OCR</a> to convert it to text, using <i><span style="font-family: courier;">tesseract --dpi 160 -l eng theImage.png output</span></i>.<br /></p><h2 style="text-align: left;">The process of building GrootGPT</h2><p>I began by telling ChatGPT what I intended to do:</p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me:</b> I want to create a program</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>That's great! I'd be happy to help you with creating a program. Could you please provide some details about the program you would like to create? Specifically, let me know what the program should do or any specific requirements you have in mind.</span></p><p><span style="font-family: courier;"><b>Me: </b>I want a simple html page which contains a chatbox that resembles the chatgpt chat</span></p></blockquote><p><span style="font-family: courier;"></span></p><p>It generated code which the browser rendered as this: <br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" 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" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Initial chatbox generated<br /></td></tr></tbody></table><p style="text-align: center;"> </p><p></p><p></p><p></p><p>However, entering text and clicking "Send" did nothing. So I asked ChatGPT to add that functionality.</p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me:</b> please modify the code so that the user can press enter to submit a chat message.</span></p><p><span style="font-family: courier;"><b>ChatGPT:</b> Certainly! To enable submitting a chat message by pressing Enter, you can add an event listener to the input field. Here's the modified code with that functionality: </span></p></blockquote><p><span style="font-family: courier;"></span><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" src="data:image/jpeg;base64,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style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Chatbox now allows input and responds<br /></td></tr></tbody></table><p>Now the chatbox responded too. It was interesting that the generated code was already primed to show messages at the left and right sides of the box.</p><p>Then I gave it a series of instructions, to each of which it modified the code to suit my requirement. Some of the instructions were: <br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><span style="font-family: courier;"><b>Me:</b> Please modify the program to have the following characteristics: Firstly, the background should be dark grey. Secondly, the title of the page should be "Groot GPT". Thirdly, the background of the chatbox should be light grey.</span></blockquote><p></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me: </b>please modify the code such that the text of the chat should be black. the chatbox and the input box should be centered. The maximum of the chatbox and input box should be 400 pixels. The chats of the first user should have a light blue background and the chats of the second user should have a pale orange background.</span></p><p><span style="font-family: courier;"><b>Me: </b>all responses and initial message from the bot should be "I am Groot". Also, please change the colour of the pale orange background to a pale green background.</span></p><p><span style="font-family: courier;"><b>Me: </b>please modify the code with the following: The edges of the chatbox need to be curved. Please make the outer border of the chatbox 5 pixels thick. Align the "Send" button's right end with the right end of the chatbox. Make the chatbox background 20% darker. Change the "Type your message" to "Chat with Groot". Provide circular profile photos for both users of the chat.</span></p><p><span style="font-family: courier;"><b>Me: </b>provide a floating menu bar at the top of the page and a thin shadow below the menu bar. The extreme right side of the menu bar should have a text "Groot GPT" and the extreme left side of the menu bar should have one buttons. The button should have text "About". The background colour of the menu bar should be the dark brown color of groot of guardians of the galaxy. The button of the menu bar should be the green color of an evergreen tree leaf.</span></p><p><span style="font-family: courier;"><b>Me: </b>you made a mistake with left and right. Please swap positions of the text and button in the menu bar. Also, please align both sides of the chat textbox and button with the chat display. If the "About" button is clicked, it should display a lightbox with the following text "Created by Navin".</span></p></blockquote><p> </p><blockquote><p><span style="font-family: inherit;">I wanted to see if it'd be able to use an image from the internet.</span><span style="font-family: inherit;"><br /></span></p></blockquote><p> </p><blockquote><p><span style="font-family: inherit;"></span></p><p></p><p><span style="font-family: courier;"><b>Me: </b>please use the icon from this page https://icon-library.com/images/small-heart-icon/small-heart-icon-17.jpg for the assistant and use the icon from this page https://icon-library.com/images/small-heart-icon/small-heart-icon-4.jpg for the user. Change position of the "about" button to the extreme left of the screen and change position of the "GrootGPT" text on the menu bar to the extreme right of the screen. Ensure that the menu bar does not cross page limits. Make the menu bar colour lighter brown. </span></p></blockquote><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" 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style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Progress so far. The lightbox content was rendered wrongly<br /></td></tr></tbody></table><p style="text-align: center;"><span style="font-family: courier;"></span></p><p>It didn't really understand the meaning of left and right. Even if it generated the code correctly once, by the time I prompted it again, it'd swap back the widgets to the wrong sides of the page.<b> <br /></b></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me:</b> I asked you to swap places of the menu bar's about button and the "groot GPT" text. Please do it.</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>Apologies for the confusion. Here's the updated code with the About button on the right and the "Groot GPT" text on the left of the menu bar: <code><br /></span></p><p><span style="font-family: courier;"><b>Me: </b>please replace both profile images with dummy image names named "profile1.jpg" and "profile2.jpg". Ensure that all chats shown on the right side are chats of the user and all chats shown on the left side are chats of the assistant. When the page is initially shown, the lightbox should not appear. The lightbox should be displayed only when the "about" button is clicked. When the lightbox is shown, the background of the entire page should be dimmed and the lightbox should be shown clearly with a light brown background and a default close button.</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>Apologies for the oversight. Here's the updated code with the changes you requested:<code></span></p><p><span style="font-family: courier;"><b>Me: </b>when the page opens, only the assistant should say "I am groot". The user should not say "I am groot". Also, please swap positions of the "About" button and the "Groot GPT" text on the menu bar. </span></p></blockquote><p><span style="font-family: courier;"></span></p><p> </p><p>ChatGPT is still unable to "understand" that I want the widgets at specific places.</p><p><br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me: </b>do you understand what i mean by swapping positions of two widgets?</span></p><p><span style="font-family: courier;"><b>ChatGPT:</b> Apologies for the confusion earlier. I misunderstood your request. To swap the positions of the "About" button and the "Groot GPT" text on the menu bar, you can update the code as follows: <code><br /></span></p><p><span style="font-family: courier;"><b>Me: </b>ok then. Please swap the positions as you have understood and ensure that all elements of the menu bar are within page limits.</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>Apologies for any confusion caused. Here's the updated code with the swapped positions of the "About" button and the "Groot GPT" text on the menu bar, while ensuring that all elements of the menu bar stay within the page limits: <code><br /></span></p><p><span style="font-family: courier;"><b>Me: </b>Display the "Groot GPT" text at the extreme left of the menu bar and display the "About" button at the extreme right of the menu bar. Ensure that the menu bar is within page limits. Make the page background color #242124. Make the "About" button color #8bc34a. Make the "Groot GPT" text bold and give it the color #c5e1a5. Make the menu bar color #324406.</span></p></blockquote><p><span style="font-family: courier;"></span></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" 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style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">By now a basic lightbox showed up well on clicking the About button<br /></td></tr></tbody></table><p> </p><p>Since a simple chat was boring, I decided to incorporate some simple sentiment analysis to display emotions.<b></b></p><p><b><br /></b></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><span style="font-family: courier;"><b>Me: </b>Could you create an algorithm that performs a simple sentiment analysis for the emotions of happiness, sadness, anger, frustration and determination?</span></blockquote><p></p><p> </p><p>But ChatGPT created the code in Python instead of JavaScript.</p><p><br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><span style="font-family: courier;"><b>Me:</b> The functions should not be in python. The functions are intended to be used in the html code you generated earlier</span></blockquote><p></p><p> </p><p>Then it generated the code in JavaScript. When I didn't understand some part of the code, I could even ask ChatGPT to explain the code to me.</p><p><br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me: </b>which part of the code will invoke getemoticon and which part of the code will send the user input to the getemoticon function?</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>To invoke the getEmoticon() function and send the user input for sentiment analysis, you can modify the existing JavaScript code that handles user input and chat message rendering. Here's an example of how you can incorporate the getEmoticon() function into your JavaScript code: <code> In the above code, the handleUserInput() function is called when the send button is clicked (or when the form is submitted, if you're using a form). Inside this function, the user input is retrieved from the input box (inputBox.value) and then cleared (inputBox.value = ''). After that, the getEmoticon() function is invoked with the user input to determine the appropriate emoticon. The resulting emoticon is then appended to the chat message element, which is created and styled accordingly. The chat message element is then appended to the chat box. You can adjust this code according to your specific implementation, but this should give you an idea of how to incorporate the getEmoticon() function and handle user input in your existing JavaScript code.</span></p></blockquote><p><span style="font-family: courier;"></span></p><p><br /></p><p>I soon realized that I didn't have to type out different words. ChatGPT could automatically find and list out words related to a particular emotion. <br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><span style="font-family: courier;"><b>Me: </b>For happiness, you had generated var happinessKeywords = ["happy", "joyful", "excited", "delighted"];. I want you to generate a hundred words that represent happiness in various tenses.</span></blockquote><p></p><p>It generated a vertical list of bulleted points containing a hundred words that represent happiness. So I had to ask it to put those words in an array, and then asked it to generate words for other emotions too.<br /></p><p><span style="font-family: courier;"></span></p><blockquote><span style="font-family: courier;"><b>Me:</b> place all the 100 generated words in an array like this var happinessKeywords = ["happy", "joyful", "excited", "delighted"];</span></blockquote><p></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><span style="font-family: courier;"><b>Me: </b>Generate a hundred sadness keywords like this var sadnessKeywords = ["sad", "unhappy", "depressed", "disappointed"]; in an array in a single line. </span></blockquote><p></p><p>Since it generated the entire code after each prompt, I started asking it to generate only snippets of the code that needed modification. But this brought the dilemma of understanding where to insert the code manually. I realized that I could ask it to give me hints for that too.<br /></p><p><span style="font-family: courier;"><b></b></span></p><blockquote><p><span style="font-family: courier;"><b>Me: </b>which part of the code should i use the window.onload function?</span></p><p><span style="font-family: courier;"><b>Me: </b>I want you to adjust the css of input-box in such a way that the input box and the send button are shown at the centre of the chatbox</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>To center the input box and the send button within the chatbox, you can apply CSS styles to the input-box class. Here's an example of how you can modify the CSS:</span></p><p><span style="font-family: courier;">.input-box {<br /> display: flex;<br /> justify-content: center;<br /> align-items: center;<br />}<br />This CSS code uses flexbox properties to horizontally center the content of the input-box class. The justify-content: center property centers the content along the horizontal axis, and the align-items: center property centers the content along the vertical axis. Make sure to add this CSS code within the <style> tags or your CSS file. By applying these styles, the input box and the send button will be positioned at the center of the chatbox. <br /></span></p><p><span style="font-family: courier;"><b>Me: </b>that did not work. the input box and button are still aligned to the left of the chatbox. How to align them to the centre?</span></p><p><span style="font-family: courier;"><b>ChatGPT: </b>Apologies for the confusion. If the previous CSS code didn't work as expected, you can try the following approach to center the input box and send button within the chatbox:</span></p><p><span style="font-family: courier;">.input-box {<br /> text-align: center;<br />}<br /><br />.input-box input[type="text"],<br />.input-box button {<br /> display: inline-block;<br />}<br /><br />.input-box button {<br /> margin-left: 10px; /* Adjust the margin as needed */<br />}<br /></span></p><p><span style="font-family: courier;">In this updated CSS code, we set the text-align property of the .input-box class to center to horizontally align the content within the input box and send button. Additionally, we use display: inline-block for the input text and button elements to allow them to be horizontally centered. You can adjust the margin-left property of the button to add spacing between the input box and the button if needed. Remember to place this CSS code within the <style> tags or your CSS file. Please give it a try and let me know if it works as expected.</span></p></blockquote><p><span style="font-family: courier;"></span></p><p><span style="font-family: courier;"></span></p><blockquote><b>Me: </b>how would you alter the code if you wanted to make it mobile friendly?</blockquote><p> For the profile images, I used <a href="https://stablediffusionweb.com/" target="_blank">StableDiffusionOnline</a>.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_8YXxQjQDLKRQtl_Cvr5aOnutm_hLSDL9EQHHgEJlj3dCsVBb_AE4xQP4_45Xoxf0FKGdEkDXtO_vDXnwe1PLLeqGpp2iAfRCcue7cbErMWKzkALo1ntdEu2gRu670z259x3zTK2w9QOKVw-bWE06BFGZ9T7CpU0tK3nRNd5hw7_7i6FOVvIX4RbtDEY/s600/stableDiffusionGroot1.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="297" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_8YXxQjQDLKRQtl_Cvr5aOnutm_hLSDL9EQHHgEJlj3dCsVBb_AE4xQP4_45Xoxf0FKGdEkDXtO_vDXnwe1PLLeqGpp2iAfRCcue7cbErMWKzkALo1ntdEu2gRu670z259x3zTK2w9QOKVw-bWE06BFGZ9T7CpU0tK3nRNd5hw7_7i6FOVvIX4RbtDEY/s16000/stableDiffusionGroot1.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Some of the profile images generated on prompting StableDiffusionOnline<br /></td></tr></tbody></table><br /><p></p><p>In this manner, I made it generate code to add a visitor counter, made the page more mobile friendly and made various adjustments that brought it to the final state you see in <a href="https://nav9.github.io/grootgpt/" target="_blank">this page</a>. The code that was finally generated is available <a href="https://github.com/nav9/grootgpt" target="_blank">here</a>.<br /></p><p>It took 70 prompts, around 5 hours (including <a href="https://nrecursions.blogspot.com/2020/11/the-real-cure-for-eye-strain.html" target="_blank">my eye rest breaks</a>) and a few manual tweaks to the code, to create GrootGPT.</p><h2 style="text-align: left;">Learning <br /></h2><p>At the time I created this, I hadn't gone through the various guides on Prompt Engineering. I tried communicating in English, since my objective was also to later add a voice prompt to invoke ChatGPT via an API. The process of creating the page showed me some critical flaws of Large Language Models not really understanding what was told to them. The "understanding" was merely a complex form of <a href="https://en.wikipedia.org/wiki/Cosine_similarity" target="_blank">Cosine Similarity</a> and token prediction. <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)" target="_blank">Transformers</a> are indeed impressive, and GPT4 is said to be capable of <a href="https://www.youtube.com/watch?v=aG5aODEhXfc" target="_blank">generating a website from a scribble</a>. However, GPT is still like <a href="https://en.wikipedia.org/wiki/ELIZA" target="_blank">Eliza</a>. "Intelligence" is still in our brains. We are yet to find a way transfer it to algorithms. However, as GitHub Copilot and similar projects have shown, Machine Learning algorithms are great for assisting us and reducing the grunt-work we go through.</p><p>It's not necessary that big-tech companies rule the roost when it comes to generative AI. Projects like <a href="https://gpt4all.io/index.html" target="_blank">gpt4all</a>, <a href="https://freedomgpt.com/" target="_blank">Freedom GPT</a>, <a href="https://www.privategpt.io/" target="_blank">privateGPT</a> and frameworks like <a href="https://www.modular.com/mojo" target="_blank">Mojo</a> hold promise.After Meta made <a href="https://ai.facebook.com/blog/large-language-model-llama-meta-ai/" target="_blank">LLaMA</a> open source, Google is said to have <a href="https://www.semianalysis.com/p/google-we-have-no-moat-and-neither" target="_blank">felt the pinch</a>. would big-tech really let their shareholders down? There has perhaps been a slow shift toward more protective licensing of their projects. Let's see how it goes. What I'd really like to see though, is a research effort that attempts understanding and building what we could actually call "intelligence".</p><h2 style="text-align: left;">Uses of ChatGPT</h2><p>From an internet search, I figured ChatGPT could be used for: </p><ul style="text-align: left;"><li><b>Coding: </b>Debugging code, creating templates etc.</li><li><b>School work:</b> Creative writing, etc.</li><li><b>Learning: </b>Having ChatGPT teach how to code or learn a new human language.</li><li><b>Feedback: </b>You can ask ChatGPT for its opinion on short stories and poems.</li><li><b>Brainstorming: </b>Generating ideas for poems, stories or games.</li><li><b>Random questions: </b>You'd have to verify the veracity of the answers though.</li><li><b>Daily tasks: </b>Recipes, to-do lists, reminders.</li><li><b>Entertainment: </b>For jokes, generating fictional stories.</li><li><b>Guide:</b> It could be used for therapy or coaching. A doctor said that the response from ChatGPT was better than him, at pacifying the annoying relatives of a patient who was being treated.</li><li><b>Research: </b>I found it useful to feed it my research paper and ask it to generate an abstract, title and conclusion which I later modified.</li><li><b>Companion:</b> For some people, it's useful to just have someone to talk to. </li></ul><p>It's important to verify what ChatGPT generates. Lawyers already <a href="https://www.google.com/search?client=firefox-b-lm&q=lawyers+in+trouble+due+to+chatgpt#ip=1" target="_blank">got into trouble</a> for using ChatGPT, which generated imaginary cases. </p><p>Let's hope the alternatives to ChatGPT offer some worthy competition too. There's Bard, Sparrow, ChatSonic, and <a href="https://github.com/steven2358/awesome-generative-ai" target="_blank">more</a>. There's an AI for <a href="https://theresanaiforthat.com/" target="_blank">a lot of things</a> now. Even a website like <a href="https://tinywow.com/" target="_blank">tinywow</a> which has free versions of various tools.<br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-68391606683519539162023-06-08T18:22:00.003+05:302023-07-21T19:22:23.499+05:30When can we use spoken English as a programming language?English has become unnecessarily complicated. Simplifying it will also help simplify AI training and daily use. What would you like to simplify about English?<p>Husbands across the world are familiar with how their wives use English or any other human language (or a broom) to “program” them into doing exactly what they want. The “code” in your mind is also getting re-programmed as you read this article. Such “dynamic syntax and semantics” which actually understands information and context, is ideally what we want computers to be capable of. Why aren’t programming language creators channeling efforts in this direction?<br /><br />At my first computer class, our teacher said: “the computer is a dumb machine. It only does exactly what you tell it”. Although programs like <a href="https://www.google.com/search?client=firefox-b-lm&q=flappy+bird+built+using+chatgpt" target="_blank">Flappy Bird</a> and <a href="https://nav9.github.io/grootgpt/" target="_blank">GrootGPT</a> have been built by prompting ChatGPT in English, and with GPT-4 generating websites using a <a href="https://www.google.com/search?client=firefox-b-lm&q=gpt+4+built+website+from+image" target="_blank">diagram as a prompt</a>, I do believe we are getting closer to being able to program computers by simply talking to them.</p><p></p><blockquote><i><b>Don’t do the work. Make the computer do the work for you — Nav</b></i></blockquote><p></p><h2 style="text-align: left;">The redundancy of memorizing syntax and semantics</h2><p>Look at the various ways programmers had to learn and re-learn how to write a simple loop, because programming language creators decided to let their imagination go wild, instead of thinking about the end user:</p><ul style="text-align: left;"><li>Repeat 3[Forward 100 right 90]</li><li>for(int x = 0; x<=3; x++)</li><li>FOR i = 1 TO 3</li><li>for (let i = 0; i < a.length; i++)</li><li>for i := 1; i <= 3; i++</li><li>for x in data:</li><li>for a in 1..3 do</li><li>for i in {1..3}</li><li>for a = 3.0: -0.1: 0.0</li></ul><p>As programmers gain experience, learning the wanton fluctuation in syntax and commands gets mentally exhausting. Creators of frameworks and programming languages really need to put in effort to provide simplicity and familiarity for the users. Either that, or build an AI to handle it.<br /><br />It’s the same with conditional statements, classes and datastructures. In one language you learn the length of an array is <i>array.length()</i>. In another language it’s <i>len(array)</i>. With for loops, what if we could just tell a computer to…</p><p><b>loop three times</b><br /><br />…and the computer would understand it and automatically translate it to any high-level or low-level language we wish? The same applies to the unnecessary complexities of web frameworks. It’s mentally exhausting to be forced to learn all the nuances of PHP, then Struts2, then Spring framework and then realize that you also have to learn a different bunch of nuances of Django for a new project. Unfortunately, even new frameworks/languages like Julia and Modular’s Mojo are busy building a new syntax.<br /><br /><b>The future: </b>Being able to talk to an IDE to do programming is just the first step. Improved versions will have us talking and interacting with (or even mind-controlling) not just a visualization of the final product on the screen (rather than deal with the code), but we’d be touching and interacting with what we create, via Virtual and Augmented Reality. Complexities and details will seamlessly be handled by an AI.</p><p></p><blockquote><i><b>As programmers gain experience, learning the wanton fluctuation in syntax and commands can get mentally exhausting if one has to switch between languages multiple times. Creators of frameworks and programming languages really need to put in effort to provide simplicity and familiarity for the users. Either that, or build an AI to handle it.</b></i></blockquote><p></p><h2 style="text-align: left;">The digital debt of repetitive manual tasks</h2><p>As a full-stack developer, I’m fed up of manually having to start servers or write scripts to do it or setup manifest files and manually plan and test out the connectivity of disparate web technologies. Then there are the errors, compatibility issues and bugs that show up repeatedly which again require manual intervention. Then there’s the need to type and memorize commands like “kubectl run -i …” and also memorize which other parameters require a double dash “ — tty”.<br /><br />These are all things that an AI assistant algorithm should be handling. Human intelligence and intervention is of course required, but a lot of the repetitive work can indeed be handled by an AI. This means massive cost savings not just in effort, but also in having reliable software (thus not losing time in debugging and also in not losing customers).</p><p>But we are too busy to build this, aren’t we?</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgXfB8S9sJgXTtsjILPWJOwVMPqCGWgvBK5LuGTKMVFKt_QEO4jOHVp84BQq7P0dtjqdbHfDxEsZ3Cl5IXe-IqdV5_xq-rAc2TAoLkY7Eq5rwM0D66w3G_JxVr7yxMsH2QiKzuuWjQJimAQhCrq9A7zth-dn4YeIeM5EV7KGLoEVcyGKwdROo3TIwfY/s916/tooBusy.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="271" data-original-width="916" height="189" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgXfB8S9sJgXTtsjILPWJOwVMPqCGWgvBK5LuGTKMVFKt_QEO4jOHVp84BQq7P0dtjqdbHfDxEsZ3Cl5IXe-IqdV5_xq-rAc2TAoLkY7Eq5rwM0D66w3G_JxVr7yxMsH2QiKzuuWjQJimAQhCrq9A7zth-dn4YeIeM5EV7KGLoEVcyGKwdROo3TIwfY/w640-h189/tooBusy.png" width="640" /></a></div><br /><p></p><h2 style="text-align: left;">Simplifying English as a first step</h2><p>Haven’t you always wanted to just say “eye doctor” instead of having to spurt out “ophthalmologist”? Do we really need words like “yeet” when “throw” communicates it just fine? English is a language that borrowed from many other languages and grew massive with time. There’s a <a href="https://softwareengineering.stackexchange.com/questions/16836/how-do-programming-languages-benefit-from-being-based-on-english" target="_blank">prominence</a> of English used in programming languages, and it would help to simplify it.<br /><br /><b>Time: </b>Was it really necessary to have 12am and 12pm? Isn’t 12:00hrs and 00:00hrs so much clearer in referring to day and night? Even clocks don’t need to have the pointless hour and minute hand where we are expected to do additional mental processing to interpret 6 as 30. It’s just silly!<br /><br /><b>Months & days: </b>Couldn’t we have month names as Month1, Month2, instead of January, February? Couldn’t we have days of the week as Day1, Day2? I still find it hard to figure out what day of the week people are referring to in regional languages.</p><p><b>Planets: </b>Mothers would be glad to not have their seating choices questioned, with mnemonics like My (Mercury ) Very (Venus) Elegant (Earth) Mother Just Sat Upon Nine Porcupines. Planets could simply be named Planet1, Planet2, and so on.<br /><br /><b>Spelling:</b> Couldn’t we write words the way they are pronounced? “Kayos”, instead of “Chaos”, “Kernel” instead of “Colonel” (or just have military ranks as Rank1, Rank2A, …). Shorten unnecessarily long words like “Abbreviation” into “Abbr”? Get rid of the dots in “E.g.” and “etc.”.<br /><br />I know it appears weird at first, but when you think about it, you’ll realize that we’ve just been conditioned into accepting and normalizing weird spellings and conventions.</p><p><b><i></i></b></p><blockquote><b><i>English has become unnecessarily complicated. Simplifying it will also help simplify AI training and daily use. What would you like to simplify about English?</i></b></blockquote><p></p><h3 class="graf graf--h3" name="5755">Reality of “AI” not being intelligent</h3><h2 style="text-align: left;"></h2><p class="graf graf--p graf--startsWithDoubleQuote" name="0112">“Intelligence” is still in the human brain. We haven’t really managed to make Generative AI or Artificial Neural Networks, truly intelligent. <b class="markup--strong markup--p-strong">Using probability values to predict tokens is definitely not intelligence.</b> When building <a class="markup--anchor markup--p-anchor" data-href="https://nav9.github.io/grootgpt/" href="https://nav9.github.io/grootgpt/" rel="noopener" target="_blank">GrootGPT</a>, I noticed how ChatGPT didn’t actually understand what I meant by the left or right side of the page. It didn’t understand enough to modify the generated code to make the lightbox disappear when the User clicks outside the box. Colleagues have told me how ChatGPT would import imaginary packages in Python. Even though the results of current state-of-the-art of Machine Learning is impressive, the direction of research needs to be altered to build systems that are capable of independent logical analysis and adaptive learning. <b class="markup--strong markup--p-strong">As few researchers have observed (and even I presented in my thesis), this requires allowing the AI to experience the world in the way we do</b>. It is difficult to achieve, but I’m sure it is possible if people have the courage to break free from conventional thinking.</p><p class="graf graf--p" name="0dac">Communicating without errors with humans using English is tough in itself. An algorithm will need to take in a lot more context. Still, let’s see if it’s the open source community, academia, an independent researcher, or a corporate that builds the first truly intelligent algorithm. It should be incentivized. Although we have <a class="markup--anchor markup--p-anchor" data-href="https://en.wikipedia.org/wiki/Robotic_process_automation" href="https://en.wikipedia.org/wiki/Robotic_process_automation" rel="noopener" target="_blank">RPA</a>, no-code frameworks and convention-over-configuration, we are still spending a lot of time and effort re-building and fixing things. <span class="markup--strong markup--p-strong">This is not how life should be lived. We need to create more spare time to pursue our pleasures/interests and spend time with the people we love. To live, rather than just keep working long hours everyday! </span>For a start, couldn’t we use existing tech to automate-away a lot of drab, monotonous tasks and build interfaces to command them with our voice? At least I’m going to try…</p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-35823656819606358942023-05-25T11:44:00.003+05:302023-05-30T21:55:01.074+05:30Switching to Mint<p>I got fed up of the bugs in Ubuntu and switched to Mint. Here are some things I liked about it. </p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd70LlMIjZaNYSQbJuzitL9KFdf6Kvt4uAhRyPZhfsF2jKavZHFl2IhCZu0n8f2bRdqvGI4DMbo1ea6dBOx4mFP-HaNUKabHywQuMQjyHf3BdAll8ojE-NhzlNzRKaFKOhWzXVvTWCEOZdIWxRK6wsfKjWtg1tHy_YMM3cRKGONY1R70up7uU8XXZK/s2048/Linux_Mint_logo_without_wordmark.svg.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="2048" data-original-width="2048" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjd70LlMIjZaNYSQbJuzitL9KFdf6Kvt4uAhRyPZhfsF2jKavZHFl2IhCZu0n8f2bRdqvGI4DMbo1ea6dBOx4mFP-HaNUKabHywQuMQjyHf3BdAll8ojE-NhzlNzRKaFKOhWzXVvTWCEOZdIWxRK6wsfKjWtg1tHy_YMM3cRKGONY1R70up7uU8XXZK/w200-h200/Linux_Mint_logo_without_wordmark.svg.png" width="200" /></a></div><br /><ul style="text-align: left;"><li>Setting up the computer wizard. </li><li>Snapshots.</li><li>Pinning and starring important folders. <br /></li><li>Checking for drivers.</li><li>So many better backgrounds. </li><li>Cool system sounds that are much better than Ubuntu. </li><li>Setting up firewall graphically. </li><li>Intro to software center. </li><li>Showing how to contribute.</li><li>More detailed system info. </li><li>Accessible crash reports.</li><li>Able to extend the duration of data in System Monitor. <br /></li><li>None of the ugly pink color. </li><li>Terminal fore and background is the right color.</li><li>Shows occupied disk space graphically. </li><li>Easier to switch off system sounds and none of the annoying drumbeat. amplification.</li><li>No amazon bloatware.</li><li>No more notification messages that block the screen.</li><li>No desktop icons shifting positions and can paste to and from desktop.</li><li>Can easily adjust the positions and distances of icons.</li><li>Better workspaces.</li><li>Preview on icon hover from the taskbar. <br /></li><li>Applets for the system tray.</li><li>Lesser loading time. Shutdown in fewer clicks. </li><li>Good ol' Ctrl+Alt+L for lock screen.</li><li>Really helpful community. <br /></li></ul><p>...and so much more! Linux Mint OS was built for the Desktop PC, and to keep the interests of the Users in mind. <br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-26112780171135747282023-05-02T18:21:00.051+05:302023-06-26T00:00:20.088+05:30Finding work when companies and society have a poor support system<p>The recent mass layoffs at big-tech companies are proof that one shouldn't link their emotions, loyalty or future to a company. It's important to be independent. With the advent of Artificial Intelligence (or rather, Machine Learning) algorithms, the redundancy of older jobs and the creation of new types of jobs are likely. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbTxSTZUcgZlbzokGHhLu-twXO3u9I88nZCrr6tBfmIzKVhlOipSIOQZNRV98wFil6IBdF9ajrhJnEgkC_ArBvLKbOFCt-ejiGxrOcusXjVXi9wqS9_EwAu1lnDGsRErxB6t0TJWlMEvsY4DsRB-C176gkQ5li1MRDg75SsUdek7x5o95ZJ5iSjNrM/s391/freelancer.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="261" data-original-width="391" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbTxSTZUcgZlbzokGHhLu-twXO3u9I88nZCrr6tBfmIzKVhlOipSIOQZNRV98wFil6IBdF9ajrhJnEgkC_ArBvLKbOFCt-ejiGxrOcusXjVXi9wqS9_EwAu1lnDGsRErxB6t0TJWlMEvsY4DsRB-C176gkQ5li1MRDg75SsUdek7x5o95ZJ5iSjNrM/s320/freelancer.jpg" width="320" /></a></div><br /><p></p><p>However, life isn't all that simple. One can be injured and end up with a permanent disability, one could <a href="https://nrecursions.blogspot.com/2020/11/the-real-cure-for-eye-strain.html" target="_blank">suffer a burnout</a> that makes it difficult to work long hours, one could have aging parents or a family member who needs to be taken care of. These require a support system in society (helpful neighbours, friends and relatives) and a support system in the job market. </p><p><b>This is one reason I created <a href="https://github.com/nav9/FreelanceAndRemoteWorkList" target="_blank">this list of freelance opportunities that people could use (click link)</a>.</b><br /></p><p> </p><p>Despite this, there are some things that people need to learn from a young age: <br /><b>1. Become financially independent ASAP:</b> Avoid pitfalls like loans, mortgages, pointlessly expensive holidays, expensive things, too many kids and financially draining relationships.<br /><b>2. Network:</b> Spend time in building not only good relationships that can get you jobs, but also building and enriching the freelance world where one can focus on bits of work and leisure at ones own convenience. Remember however, that networking isn't just about getting benefits for yourself. You need to provide value to your network too.<br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-3904082109470205782023-04-11T07:03:00.018+05:302023-07-16T13:55:23.154+05:30Tips for building a PC yourself<p>It's 2023, and even now there's not a single website that provides all the necessary selectors or an AI which can help users identify, compare and select PC parts based on what has been successful in the market and based on which of those parts are actually in stock at retailers or wholesalers.</p><p>There are however some tools and tips I'd like to share, which can help newbies. My old Gigabyte motherboard (bought in 2012) stopped working recently for no apparent reason, when I resumed it from suspend mode. There were no POST beeps, and the CPU fan would twitch, but wouldn't start. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHRzxFMWXJfzF3tvscD37J78Qlinrv19mmIWktXQXCAjxqVRa18zOdm1Oi5hXZ0WF9QQmiaEJKLs3E-3kYxH-jCERgZDqx_m_nJB0wUWmYGja55ZAt9m7POnRR6BV9CQrwpwn0CrxlR3qEEb6he1ByZIZ-6AZVSSmHzIJwr1uj20hwF3yWpB6ersIz/s100/cpu_fan_twitch.gif" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="92" data-original-width="100" height="92" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHRzxFMWXJfzF3tvscD37J78Qlinrv19mmIWktXQXCAjxqVRa18zOdm1Oi5hXZ0WF9QQmiaEJKLs3E-3kYxH-jCERgZDqx_m_nJB0wUWmYGja55ZAt9m7POnRR6BV9CQrwpwn0CrxlR3qEEb6he1ByZIZ-6AZVSSmHzIJwr1uj20hwF3yWpB6ersIz/s1600/cpu_fan_twitch.gif" width="100" /></a></div><p></p><p>If I rotated the fan by hand, it'd continue spinning on its own lazily. These problems probably started after an automatic BIOS update (which probably happened because of Windows 10 being present), and the motherboard's sudden stoppage appeared to be due to planned obsolescence. There are quite a lot of other people posting similar problems on Tom's Hardware forums. </p><p>So the PC upgrade began with a new SMPS. </p><h2 style="text-align: left;">Buying an SMPS/PSU</h2><p>It is said that the length of warranty a manufacturer provides, is an indicator of the quality of components used in an SMPS. <b>Check for overvoltage protection and short-circuit protection</b>. There's no need of going more than 150W above your power needs. A bad PSU can erupt in fire.<br /></p><div class="separator" style="clear: both; text-align: center;"><iframe allowfullscreen="" class="BLOG_video_class" height="266" src="https://www.youtube.com/embed/2tFQ7QYkfwY" width="320" youtube-src-id="2tFQ7QYkfwY"></iframe></div><br /><p></p><p>On researching various available brands, these were some points I noticed from various reviews:</p><p>There are published lists of the quality/efficiency of PSU's:</p><p></p><ul style="text-align: left;"><li>Tiers as of March 2023: <a href="https://cultists.network/140/psu-tier-list/">https://cultists.network/140/psu-tier-list/</a></li><li>The tiers show that brand name is not enough when selecting a PSU. You have to go into more details about each specific PSU.</li></ul><p></p><p>You can verify the 80+ certification of a PSU (<b>a lower wattage SMPS is <a href="https://www.techreviewer.com/best-tech/intel-11400f-power-supply/" target="_blank">more power-efficient</a> when idle, so you can save electricity by getting the correct wattage instead of a higher 80+ rating</b>):</p><p></p><ul style="text-align: left;"><li>I doubted that Zebronics actually had an 80+ certification, but I checked on the official ClearResult website, and saw that it did: <a href="https://www.clearesult.com/80plus/manufacturers/115V-Internal">https://www.clearesult.com/80plus/manufacturers/115V-Internal</a></li><li>The <a href="https://en.wikipedia.org/wiki/80_Plus" target="_blank">80+ certification</a> of PSU's usually tell you the efficiency of the PSU under full load. The efficiency may or may not be different when under lower load.</li></ul><p></p><p>There are websites that offer an SMPS power calculator (many may not be very accurate):</p><p></p><ul style="text-align: left;"><li>Said to be good: <a href="https://outervision.com/power-supply-calculator">https://outervision.com/power-supply-calculator</a></li><li><a href="https://www.reddit.com/r/pcmasterrace/comments/2xqtbi/neweggs_psu_wattage_calculator_is_glorious/" target="_blank">Not very accurate</a> and buggy too: <a href="https://www.newegg.com/tools/power-supply-calculator/">https://www.newegg.com/tools/power-supply-calculator/</a></li><li>Unsure if it's accurate. Not many options: <a href="https://www.coolermaster.com/power-supply-calculator/">https://www.coolermaster.com/power-supply-calculator/</a></li><li>Switch to English on the top right: <a href="https://www.bequiet.com/en/psucalculator">https://www.bequiet.com/en/psucalculator</a></li><li>Beta version, so should be inaccurate: <a href="https://seasonic.com/wattage-calculator#">https://seasonic.com/wattage-calculator#</a></li><li>Does a fair job, but not necessarily accurate: <a href="https://pcpartpicker.com/list/">https://pcpartpicker.com/list/</a></li><li><a href="https://www.fsplifestyle.com/landing/calculator.html">https://www.fsplifestyle.com/landing/calculator.html</a></li><li>For older computers: <a href="http://powersupplycalculator.net/">http://powersupplycalculator.net/</a></li></ul><div><h3 style="text-align: left;">Observations from reviews:</h3></div><div><ul style="text-align: left;"><li><b>Ant ESports:</b> Said to be of cheap quality. Unstable voltage.</li><li><b>Antec: </b>Reviews say it has bad customer support, missing cables, damaged cables, and the SMPS blew up for one person.</li><li><b>CoolerMaster: </b>Not necessarily good, but may have some good models.</li><li><b>Corsair:</b> Was said to be good earlier, but suspected to have cheap parts now, since more than one customer reported a sparking noise and for one person it failed in a year. The plugs are not meant for India (I don't understand why they can't supply a different plug), so you need an adapter. Said to have good customer support (which is handled by a franchisee in India).</li><li><b>Zebronics:</b> It blew up for at least one person. Couldn't handle sustained load. The higher power SMPS actually had an 80+ certification.</li><li><b>Artis:</b> One of the cheaper quality PSU's. A user had trouble waking from suspend. No fluctuation protection.</li><li><b>NZXT:</b> Said to be good. Acro Engineering in Bangalore is the service centre franchisee. They were responsive. Comes with a ridiculously huge 16A power plug which won't fit into any of your 6A sockets. However, even their official customer support in Spain was helpful and responsive. They confirmed that for my power needs, I could use an old 6A power cord I had.<br /></li><li><b>Asus:</b> Good quality and good warranty. However the Bangalore service centre does not handle SMPS servicing.</li><li><b>EVGA:</b> Said to be good and said to honour their warranty.<br /></li><li><b>MSI: </b>Could be good. Service centre was responsive, but background noise at the centre didn't allow me to confirm if they handled SMPS servicing.</li><li><b>Seasonic: </b>Among the best. But expensive. One User said his SMPS failed within a year and he had to ship it to Kolkata to get it replaced. But the entire process of replacing it took just one week.<br /></li><li><b>ThermalTake:</b> Among the best. But prohibitively expensive.</li></ul><div><h3 style="text-align: left;">Precautions:</h3></div></div><div><ul style="text-align: left;"><li>Make sure you buy from a reputed brand that offers a 5+ year warranty (Seasonic and ASUS sometimes offer 6+ year warranties). A stable voltage supply, various power conservation features and part quality matter, when it comes to keeping your motherboard and peripherals safe.</li><li>For the wattage, seek out an SMPS whose wattage number is "<a href="https://www.makeuseof.com/tag/6-things-know-buying-power-supply-unit-psu/" target="_blank">continuous</a>" (steady maintenance of that wattage) rather than "peak" (can reach that wattage only for short periods). </li><li>A multi-rail SMPS is advised if there's high voltage fluctuation in your area. There are three basic rails: +3.3V (PCIe/AGP, chipset and some DIMM), +5V (PCI/AGP, low voltage motors, SIMM), and +12V (PCIe/AGP, high voltage regulators, motors). SB is Standby voltage.</li><li>See if it helps to plug the SMPS into a voltage stabilizer.</li><li>Understand and verify the documentation about the pin connections that are required for your motherboard. I remember a person who had to put his 600W PSU for sale online because he didn't check the number of power connections his motherboard required.</li><li>Check for the number of SATA power connectors and molex connectors. <br /></li><li>PSU's like Seasonic and NZXT come with a 16A plug, which won't fit into any 6A sockets in your room. See the pic below (<i>the service centre said it's ok to use a 6A power cord, and online
reviews say that you shouldn't use a 6A adapter with a 16A cord, since
the current and wire thickness difference can cause overheating</i>). <br /></li></ul></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxjIiY6n5JuS5gkNEnFJAZRY7ocNoSkQ3Z_k4LlRK95aM5Voi36xeTDHR3vVnylvuYzrWdufVtGmYqBXn2Fc0trpk5R-F4-td04hlDjz8db3lcOWPdT9pST6laYJY8ocHFsdHCvNDd_3gedb9egSHUBGIva8ukEUjMU2HJrCGRdk1XINKHCeVCiFXu/s300/plugs_16A_6A.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="252" data-original-width="300" height="252" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxjIiY6n5JuS5gkNEnFJAZRY7ocNoSkQ3Z_k4LlRK95aM5Voi36xeTDHR3vVnylvuYzrWdufVtGmYqBXn2Fc0trpk5R-F4-td04hlDjz8db3lcOWPdT9pST6laYJY8ocHFsdHCvNDd_3gedb9egSHUBGIva8ukEUjMU2HJrCGRdk1XINKHCeVCiFXu/s1600/plugs_16A_6A.jpg" width="300" /></a></div><br /><div><h2 style="text-align: left;">Buying RAM</h2></div><div>Don't be tricked by the RAM frequency shown. For DDR4 RAM, the base frequency (the frequency it runs at during normal operation) it runs at is 800-1600MHz. The effective frequency (when you enable XMP in BIOS) is 1600-3200MHz.</div><div> </div><div><h3 style="text-align: left;">Precautions: </h3></div><ul style="text-align: left;"><li>Also, be aware of <a href="https://www.cgdirector.com/wp-content/uploads/media/2022/02/RAM-Latency-Table.jpg" target="_blank">RAM latency</a>. Check the CL number printed on the RAM.</li><li>Make sure the motherboard you choose, supports the RAM version and frequency. </li></ul><h3 style="text-align: left;">Some brands: <br /></h3><ul style="text-align: left;"><li>Corsair (likely a good brand. Origin: Taiwan)</li><li>GSkill (probably a good brand. Origin: Taiwan). They were one of the first and probably only brands to ensure <a href="https://en.wikipedia.org/wiki/G.Skill" target="_blank">Rowhammer</a> safety.</li><li>Crucial (probably good, but these usually have a higher CL number, so are likely to be slower, so don't go by just the price)</li></ul><div>RAM usually works well for very long, unless overclocked frequently. So even if you can buy second-hand RAM, it's ok.</div><div> </div><div><h2 style="text-align: left;">Buying a processor</h2></div><div>I used to think that Intel processors were superior, but I was wrong. AMD seems to be manufacturing better processors. When selecting a processor, it helps to make a table containing the processor price, name, speed (normal/boost), socket type, L1/L2/L3 cache sizes, wattage (base/max), cores and threads per core, launch date, maximum RAM and maximum channels. The smaller the <i>nm</i> of the processor, the more transistors they can fit into a chip, and the more power efficient the processor will be. If you aren't into serious gaming, it also helps to have a processor with an integrated GPU.</div><div><b>Note:</b> People say that you need to change the thermal paste often. That's probably a myth like how dentists say you need to visit them every 6 months. <br /></div><div><br /></div><div><h2 style="text-align: left;">Buying a motherboard</h2></div><div><b>Search for "motherboard nomenclature"</b>, and you'll see articles mentioning what the "Prime", "TUF", "ROG" etc. portions of the board name means. For example:</div><div>MSI PRO boards are lower quality compared to MSI MAG boards. The order is as follows, with the names on the left of the less than sign being worse quality.</div><div><ul style="text-align: left;"><li>PRO < MAG < MPG < MEG.</li></ul></div><div>Same way, for ASUS boards it is:</div><div><ul style="text-align: left;"><li>Prime < TUF < ROGStrix < ROG.</li></ul></div><div>The names mentioned above are the board series names. Strix E is the best. Strix F is the second best. Strix G is a Micro-ATX and Strix H is ATX. With motherboards designed for Intel chips, the G means it is GPU optimized, and S means it is storage optimized.<br /></div><div>The Z790, B650 etc. are the chipset names.</div><div>The model names are P, Gaming, ProAX, etc. </div><div><h3 style="text-align: left;">Precautions:</h3></div><ul style="text-align: left;"><li>Select the right chipset which matches the processor. Motherboard manufacturers usually publish a processor compatibility list.</li><li>If you choose a motherboard that does not support overclocking, you don't need to select a processor that supports overclocking (unless you plan to switch the processor to a different motherboard later).</li><li>The boards with WiFi are generally not worth the extra money unless you will actually use WiFi and Bluetooth. The alternative is to buy a separate WiFi card when you need one.</li><li>Go to the official motherboard specifications page and ensure it has all features you need. These include NVMe, PCIe, onboard speaker, USB ports, display ports, LAN, audio and PS2 port if you ever need it.</li><li>Have a look at the number of pins required for the CPU power, and ensure that your SMPS provides it. 8 pins is the standard, but some have a second optional power connector with 4 pins for people who want to overclock.</li><li>Check for XMP support (also called AMP, EOCP or DOCP).</li><li>LED boot lights are helpful.</li><li>Check if the processor has integrated graphics. Also called APU for AMD processor. These are CPU's with GPU's built in. Some can be so powerful, that you won't need an extra graphics card. For example, the Ryzen 5 5600G has an APU that's <a href="https://www.youtube.com/watch?v=gexxEg9TiU0" target="_blank">equivalent to</a> an NVIDIA GT 1030.</li><li>Check for warranty duration and service. </li><li>Check for whether they have a history of stable BIOS updates.</li><li>Check for BIOS flashing and BIOS reset options. </li><li>Be aware of the <a href="https://pcpartpicker.com/forums/topic/417525-can-someone-explain-this-in-english-terms" target="_blank">DPC and R terms</a>.</li><li>Look through reviews and posts on forums for complaints about particular motherboards.<br /></li></ul><div><h2 style="text-align: left;">Customer support</h2></div><div>This is an essential part of selecting a product. The customer service centers should ideally be in your city, else you'll have to ship it to another city. Before buying the product, it also helps to call up their customer support number or email, and ask some question to see how they answer it.<br /></div><ul style="text-align: left;"><li>ASUS: Said to have good service. Their Bangalore customer support is handled by F1 Infosolutions at Cunningham road.</li><li>MSI: Bangalore customer service at F1 Infosolutions, Kalyan Nagar. They said they'd probably have to send the motherboard to Delhi for inspection. Their website has a page for registering the product.<br /></li><li>ASRock: Bangalore customer service at Smartlink network systems, Jayanagar.</li><li>Coolermaster and Crucial: Bangalore customer service at Kaizen Infoserve at NR road, near Majestic. </li><li>Gigabyte: Service center is in Chennai.</li><li>NZXT: Bangalore customer service is at Acro engineering, near Lalbagh. The service person was responsive and helpful. <br /></li></ul><div><h2 style="text-align: left;">Useful sites</h2></div><div><ul style="text-align: left;"><li>Compare products: <a href="https://versus.com/en">https://versus.com/en</a></li><li>See the price variation of products listed on ecommerce websites (I have a hunch this website may be bluffing): <a href="https://pricehistoryapp.com/">https://pricehistoryapp.com/</a></li><li>Select parts: <a href="https://www.pcstudio.in/pc-build/">https://www.pcstudio.in/pc-build/</a>. This website is owned by Ankit Infotech at SP Road. Reliable and polite people. Good website. It has useful filters to help you select well. </li><li>Guidance for building a PC: <a href="https://www.reddit.com/r/buildapc/wiki/index/">https://www.reddit.com/r/buildapc/wiki/index/</a>. You can ask questions on <a href="https://www.reddit.com/r/buildapc/" target="_blank">their forum</a>.</li><li>More guidance for building a PC: <a href="https://www.tomshardware.com/how-to/build-a-pc">https://www.tomshardware.com/how-to/build-a-pc</a><br /></li></ul></div><div><h2 style="text-align: left;">Building the PC</h2><ul style="text-align: left;"><li style="text-align: left;">Do not keep the motherboard on the anti-static bags. It is <a href="https://forum-en.msi.com/index.php?threads/warning-about-anti-static-bags.131667/" target="_blank">made of conducting material</a>. You can however keep it on cardboard that has no print on it. </li><li style="text-align: left;">When tightening or removing the screws of the cooler, remove the screws diagonal to each other first. If you remove the screws adjacent to each other, the spring action can make the cooler whip outward, possibly ripping the processor out and damaging the pins.</li><li style="text-align: left;">Before removing the cooler, run the CPU for a while to get it hot. This will make the thermal paste a bit more fluid.</li><li style="text-align: left;">It's ok to have XMP enabled for the RAM in the BIOS. But it's also necessary to run Memtest for a few passes to check if the RAM is functioning right, because if the RAM isn't at the right frequency, it'll cause the PC to crash at random points of time. Even if Memtest passes during the first pass, it can detect RAM failure during the next pass. So make sure you let it run for at least 3 passes. If it detects errors, you can go back to the BIOS and lower the RAM frequency and run Memtest again. Do this until the tests pass, and that's the stable frequency you can settle for.<br /></li></ul><h2 style="text-align: left;">Selecting a UPS</h2></div><div>There's the ordinary "Online UPS", the "Line interactive UPS" and "Standby UPS". Choose carefully, based on your voltage stability needs.<br /></div><div><br /></div><div><br /></div><p></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-35283653300230362102023-04-05T16:24:00.000+05:302023-04-05T16:24:00.280+05:30How volunteering should be done<p>Many years ago, I led a team of volunteers for what is usually called a CSR activity, but in our case it was just called Employee Volunteering, or Community Relations.</p><p>I noticed a trend among corporates and employees, to volunteer for social causes without really caring about improving the situation of the people they purported to help. Everyone just wanted to show that they were helping. The photos and entertainment was more important than empathy, caring and finding solutions.</p><p>I also noticed a pattern of people being forced to volunteer and then not wanting to return back to volunteering at all. Almost eight years ago, I had created this infographic to illustrate the problem to my seniors.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsBTH5DJKQlnTocbHCpgCDB6-ckmiARJ9bTKIwBXo2uP_J1Y1h-stoWPmvyVRwQ6nJagsGsECGe67Hle-ZSHBgLkKyAnP57x62Ym3h2iQzYJWYU7D0IMZ5Lg6Wlx-cK3sO3qm3EKOF9F9Ja6aBknv_X-3pqMdDTj5XogzLVl2givH0j0YRCfLrBpI6/s563/howVolunteeringIsDone.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="414" data-original-width="563" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjsBTH5DJKQlnTocbHCpgCDB6-ckmiARJ9bTKIwBXo2uP_J1Y1h-stoWPmvyVRwQ6nJagsGsECGe67Hle-ZSHBgLkKyAnP57x62Ym3h2iQzYJWYU7D0IMZ5Lg6Wlx-cK3sO3qm3EKOF9F9Ja6aBknv_X-3pqMdDTj5XogzLVl2givH0j0YRCfLrBpI6/s16000/howVolunteeringIsDone.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">How volunteering is done by many<br /></td></tr></tbody></table><br /><p>If you point out a problem, you should also offer a solution. So the next infographic shows one solution.</p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgp0IzctAHs0erdFRpmxhhdQ0gJ0RsHS3SmMEbqykDH0KIFnTZibimCKotdoC6h0QdyJr5RquO8VqWJ7yfMoldiFRQZPipBTJ2NNRzTPpzXZoNpN7rn1GkdQcpRGeLA9HfKGIIpqD4GyOMh7egI6fOQV-ewZrrF1CypueX5og-f7-K24i_ucqx7zGoZ/s538/howVolunteeringShouldBeDone.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="402" data-original-width="538" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgp0IzctAHs0erdFRpmxhhdQ0gJ0RsHS3SmMEbqykDH0KIFnTZibimCKotdoC6h0QdyJr5RquO8VqWJ7yfMoldiFRQZPipBTJ2NNRzTPpzXZoNpN7rn1GkdQcpRGeLA9HfKGIIpqD4GyOMh7egI6fOQV-ewZrrF1CypueX5og-f7-K24i_ucqx7zGoZ/s16000/howVolunteeringShouldBeDone.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A better way to volunteer<br /></td></tr></tbody></table><br /><p>A crucial part of the "better way", is the mentoring phase. Volunteers first need to be taught how to volunteer. In every sector, there are experts who know what the key problems are and how it needs to be addressed. They need your help, and they need it in specific ways. Don't use it as a chance to entertain your employees. Use it as a chance to actually help. It may be boring to do meaningful volunteering. But that's what really helps. </p><p>The <a href="https://nrecursions.blogspot.com/2015/06/volunteering-nav-test.html" target="_blank">Nav test</a> can provide some more guidance.<br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-7004602278667963412023-03-05T09:57:00.005+05:302023-03-05T10:09:01.900+05:30 IISC Open Day 2023<p>The previous <a href="https://nrecursions.blogspot.com/2020/03/iisc-open-day-29th-feb-2020.html" target="_blank">Open Day was at the end of February 2020</a>, when Covid cases had just begun surging. After all these years, Open Day was back again to enthrall scientific minds! The great thing about a science crowd is that everybody is well behaved and organized. Even the human-trains of school kids that snaked their way through everywhere, were organized and well behaved. </p><p>Here's a video of some of the interesting exhibits:<br /></p><div class="separator" style="clear: both; text-align: center;"><iframe allowfullscreen="" class="BLOG_video_class" height="266" src="https://www.youtube.com/embed/aGM4vPJKnQs" width="320" youtube-src-id="aGM4vPJKnQs"></iframe></div><p></p><p>Although IISc put up a map of the place, and it had a grid to help figure out where we were, the map turned out to be useless, since when people photographed it and ventured to other parts of the campus, referring the map ended up making them confused. We really need a better, more responsive map that recognizes locations and perhaps even helps us plan out our path of visiting exhibits and provides info on the transport vehicles.<br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN3AXQl0L2mUE8UNqd6qgRKtl7n9--bwZ5H-ORnr9Z_CNX7v8gEHgGTQlLHvVPmJvO37Ba4gYbs6WD-0ovle7be1Arfv4Nfvp2CI6S9RZ5lynNLmHF8nWJYj-GckAG44WxxqQFuTHgFDaBTfQ0KUHoz4_4_g3lxHYSXzpqTFntDh6HcS0G2SHgqiRj/s1202/20230304_094159.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="911" data-original-width="1202" height="485" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjN3AXQl0L2mUE8UNqd6qgRKtl7n9--bwZ5H-ORnr9Z_CNX7v8gEHgGTQlLHvVPmJvO37Ba4gYbs6WD-0ovle7be1Arfv4Nfvp2CI6S9RZ5lynNLmHF8nWJYj-GckAG44WxxqQFuTHgFDaBTfQ0KUHoz4_4_g3lxHYSXzpqTFntDh6HcS0G2SHgqiRj/w640-h485/20230304_094159.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Click to view it larger<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGTPK2HEVFEc-LNxE1h5YaB8Ce_Pex1gW1GjRxHOgyCeRKmR7a8-PcGhe8tJ6VQZYvvE1LNv2nE2Crub9Y4lNgPr--sv5TJbOCfrm1Ua1yODrSb_uGWToolrU8YQLjyCdmqNpaewcAMlIh2pCnOofaawNoNG9BLaF6cBZuY0zbOi2K2yoRpZPXECgH/s400/20230304_100957.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="277" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjGTPK2HEVFEc-LNxE1h5YaB8Ce_Pex1gW1GjRxHOgyCeRKmR7a8-PcGhe8tJ6VQZYvvE1LNv2nE2Crub9Y4lNgPr--sv5TJbOCfrm1Ua1yODrSb_uGWToolrU8YQLjyCdmqNpaewcAMlIh2pCnOofaawNoNG9BLaF6cBZuY0zbOi2K2yoRpZPXECgH/s16000/20230304_100957.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Neural network meant for physics-specific calculations<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhK-c_6GDbb4IHgQ322Cjr8aim9TtKe4bdZ-AZnat35UaAb1JwouXbobS7ShrmGoltmEf1Q7pKbbf_w-lxouVMZ3gFpxOfPWUStIkx9k4UPjse--HKXXKToRLZxeY8So3h3TLI6174F2ZbeN6rslapg54pJO-esJhsDtsuo2rgeSyC7enkJvabTF-Yx/s854/20230304_101617.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="854" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhK-c_6GDbb4IHgQ322Cjr8aim9TtKe4bdZ-AZnat35UaAb1JwouXbobS7ShrmGoltmEf1Q7pKbbf_w-lxouVMZ3gFpxOfPWUStIkx9k4UPjse--HKXXKToRLZxeY8So3h3TLI6174F2ZbeN6rslapg54pJO-esJhsDtsuo2rgeSyC7enkJvabTF-Yx/s16000/20230304_101617.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An autoencoder that compresses data and de-compresses it with some loss<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9aiRQTzHZQ2vvRes44OXjcXyJRyundi5RUJoAQkcErzQMjO83VYbjV6V2X28DBbThSKztQvf_CFh0k9ASFoD5HLbl5OcjZYQupVpY0j8zWiHKTaDY7LP8deQ8zeCRdf4uwpYqbubrgkVd_bDfUdZ97V6JVFGx4JNEMqAVwvhMSRS28a9-B1ukAner/s400/20230304_103948.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="240" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh9aiRQTzHZQ2vvRes44OXjcXyJRyundi5RUJoAQkcErzQMjO83VYbjV6V2X28DBbThSKztQvf_CFh0k9ASFoD5HLbl5OcjZYQupVpY0j8zWiHKTaDY7LP8deQ8zeCRdf4uwpYqbubrgkVd_bDfUdZ97V6JVFGx4JNEMqAVwvhMSRS28a9-B1ukAner/s16000/20230304_103948.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A catapult without the conventional friction joints<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgrzFtMYYhya8HsGmvWUI0IA4X0kGg6zRNj8nRu59OvHKIqQyzqWFiroUwpUXMr3zyaJoBmiyLw-KwZXHtksATjFfcjzaryZiffYQmaqfyrU7_nY5FxdVXe0BZpYpAw_s7U7-kenbntERqswseSYx8GMJZAxu7GOzj7Qr5bM_ELJXihtN8mMJO0Tgd1/s300/20230304_105726.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="204" data-original-width="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgrzFtMYYhya8HsGmvWUI0IA4X0kGg6zRNj8nRu59OvHKIqQyzqWFiroUwpUXMr3zyaJoBmiyLw-KwZXHtksATjFfcjzaryZiffYQmaqfyrU7_nY5FxdVXe0BZpYpAw_s7U7-kenbntERqswseSYx8GMJZAxu7GOzj7Qr5bM_ELJXihtN8mMJO0Tgd1/s16000/20230304_105726.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A robot-fish fin showing how turbulence is created in water and propels the fish forward<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh51uyF9B1Q5hq0XB17yh4bfXEDrpVhRdJRLG1T2nmS1wZ-y7XwfSXeZCd_OKiX0CsT3uWzyEiOl6i8YEABUDnhBsY7c2ufxjwg9A-tFAcIHkr23EYIxbG_4LhQgMC5eMwlWLLc2Qnjgp04NyeZ8h3fAux5Qblj9A5B_HPWqs7pGsRsh85fJcFXIHVQ/s300/20230304_110729.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="261" data-original-width="300" height="261" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh51uyF9B1Q5hq0XB17yh4bfXEDrpVhRdJRLG1T2nmS1wZ-y7XwfSXeZCd_OKiX0CsT3uWzyEiOl6i8YEABUDnhBsY7c2ufxjwg9A-tFAcIHkr23EYIxbG_4LhQgMC5eMwlWLLc2Qnjgp04NyeZ8h3fAux5Qblj9A5B_HPWqs7pGsRsh85fJcFXIHVQ/s1600/20230304_110729.jpg" width="300" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An anti-gravity structure that pulls the string in various directions and can even support a load<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgznJ4iJrZ3r47SXNjnrYZCnG3Y0MGjWoqh6X-75Vx61ANMTDYsc6n83DfWIB-2_vts0f5remUAzpBHO2bosd34LKV6784Ex82I8XUVcw0QeQx7p3j3Hh2AYJb0-YwjlRo2VovZwQR9eaPWk1efqEtuSOtT5449palsxE-hoj6qe74jn6DAN5mYCkkQ/s300/20230304_110743.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="292" data-original-width="300" height="292" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgznJ4iJrZ3r47SXNjnrYZCnG3Y0MGjWoqh6X-75Vx61ANMTDYsc6n83DfWIB-2_vts0f5remUAzpBHO2bosd34LKV6784Ex82I8XUVcw0QeQx7p3j3Hh2AYJb0-YwjlRo2VovZwQR9eaPWk1efqEtuSOtT5449palsxE-hoj6qe74jn6DAN5mYCkkQ/s1600/20230304_110743.jpg" width="300" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A demonstration of immiscibility of fluids of various densities<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7c9Barzlwj6NhZX9jjwRZ-Hkf1Zk621nHJAjln2IFRkvTo_feb88cZL28M_ETSomsWfJ0kOwbHakFm1RPQ_ilA8vxz15ggZZf1AKp3l9oYBzb0FRVUqATD3mRgkW_smEIu2pvaNAlT8fzJSzIRI41wVzOjch9zBFzzIkP0Qj8SHLzO4RNWYu0_DDU/s490/20230304_112614.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="215" data-original-width="490" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7c9Barzlwj6NhZX9jjwRZ-Hkf1Zk621nHJAjln2IFRkvTo_feb88cZL28M_ETSomsWfJ0kOwbHakFm1RPQ_ilA8vxz15ggZZf1AKp3l9oYBzb0FRVUqATD3mRgkW_smEIu2pvaNAlT8fzJSzIRI41wVzOjch9zBFzzIkP0Qj8SHLzO4RNWYu0_DDU/s16000/20230304_112614.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Drones display and demo<br /></td></tr></tbody></table><br /><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3gmxpMMcwDURaLjp7YGD2ChLee7haUsQ5Z9y_HVFmGB32w3aRlV1gGcUADGAj5UMNx8L3SDwFqwcBt-wFuJ8GMNmn5HroBTTBHhbSXdzvmM-zRDYiByxk16PHcfGkzBFtYISOadp1xZ7hO4cK1YUDH9qj9M5kU068AWZJU88X9w1s6ILB49uhwjdP/s600/20230304_121236.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="229" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3gmxpMMcwDURaLjp7YGD2ChLee7haUsQ5Z9y_HVFmGB32w3aRlV1gGcUADGAj5UMNx8L3SDwFqwcBt-wFuJ8GMNmn5HroBTTBHhbSXdzvmM-zRDYiByxk16PHcfGkzBFtYISOadp1xZ7hO4cK1YUDH9qj9M5kU068AWZJU88X9w1s6ILB49uhwjdP/s16000/20230304_121236.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Silicon chip manufacture <br /></td></tr></tbody></table><br /><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLSgNjAHpuqP6qBwaiGw99uguERCQPbZJ25kJ7EkeYTpW7Jf51ALhpG6M-lmN0GXaXLI8cJommXsCjn-EQLNtLdV9czJ6UVHZpPNX3K-HtJOyBpqn5EfOxOpfM13prfYmfxmNVuovUXHVUeyPxWUkU4WbPRHEbhOL_OMgZspnwoOPp-lbnX5eSCCqq/s400/20230304_121934.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="291" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLSgNjAHpuqP6qBwaiGw99uguERCQPbZJ25kJ7EkeYTpW7Jf51ALhpG6M-lmN0GXaXLI8cJommXsCjn-EQLNtLdV9czJ6UVHZpPNX3K-HtJOyBpqn5EfOxOpfM13prfYmfxmNVuovUXHVUeyPxWUkU4WbPRHEbhOL_OMgZspnwoOPp-lbnX5eSCCqq/s16000/20230304_121934.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">You can move the arms of the robot on the left, and it'll copy the same motion on a robot placed in another room. It vibrates to let you know you have reached a boundary. Useful in robotic surgeries done remotely<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgK2KOIVz11nvXFMEbvbcS-sD2vhUAuHNx9PiYiC7tK_JLv_qUvqEOqAJeifHkLj5NEwr6ZeF_Cw4v4F-Y4DrZu7197l6rtjelsCe6rXBdJa6kp82gK3yp3frkTpSycj10MbGsQJijz4CvswtLzwynpd9mRMpAWjFnCtyagdd7LHJ3MzPwSJlQq2cLg/s600/20230304_123225.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="446" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgK2KOIVz11nvXFMEbvbcS-sD2vhUAuHNx9PiYiC7tK_JLv_qUvqEOqAJeifHkLj5NEwr6ZeF_Cw4v4F-Y4DrZu7197l6rtjelsCe6rXBdJa6kp82gK3yp3frkTpSycj10MbGsQJijz4CvswtLzwynpd9mRMpAWjFnCtyagdd7LHJ3MzPwSJlQq2cLg/s16000/20230304_123225.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">An Augmented Reality gun which you can use to play a game<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhFZ9udMdEXGOYGz32dqsxt1EjoBmGi7KcagLoB2PVL9fRk6-n0AW-_rx_q_w3RcGzV-2qji6o34-rsYogJoZl0dFFBw2UNeAmOTL2M-cilL-W5OtL-FyTJQ4Ue5JnU6lenqpn2QHoCr-x7l0K998PeO0AeOQWTxsOveMAAeQu9ZeIeTv1j1m24lgt1/s600/20230304_123605.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="366" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhFZ9udMdEXGOYGz32dqsxt1EjoBmGi7KcagLoB2PVL9fRk6-n0AW-_rx_q_w3RcGzV-2qji6o34-rsYogJoZl0dFFBw2UNeAmOTL2M-cilL-W5OtL-FyTJQ4Ue5JnU6lenqpn2QHoCr-x7l0K998PeO0AeOQWTxsOveMAAeQu9ZeIeTv1j1m24lgt1/s16000/20230304_123605.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A circuit that calculates using analog signals instead of digital. Meant to mimic the human neurons. It performs calculations with much lower power than digital processors.<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZBktfbudFQ-CUlcNEDy1fl_cvlKQUBbVEC_60FBev6O_Tytst6z1lCImGmd9Mg_2ifK7KEak30PFooZAqiVhFAmPZd3DlB22f56gu3YeBLjHT4M2nijEBfGyzeUv--NW4zyyEvwAHykudBsQjrJBErDRIlh6OTBmT3kxpX34CLS9eolcKN74ZG9kc/s600/20230304_124439.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="338" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZBktfbudFQ-CUlcNEDy1fl_cvlKQUBbVEC_60FBev6O_Tytst6z1lCImGmd9Mg_2ifK7KEak30PFooZAqiVhFAmPZd3DlB22f56gu3YeBLjHT4M2nijEBfGyzeUv--NW4zyyEvwAHykudBsQjrJBErDRIlh6OTBmT3kxpX34CLS9eolcKN74ZG9kc/s16000/20230304_124439.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A circuit assisted with quantum computing<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxADcwMKL4YnHO_cOCqleIaQfbo6p5GZCRRoNx2wVZirs4y9Jj1o4jFPtsORsV79Nu6K_yKLskoPUyT2_VM_oGVwV0kAcFlbWm_TJVq0TGFfscMQW1Xn7e2zWlipDZeKiwOdZpMv_8Ne9_Ur4Pf4m10AIdP5-9OVsrSpSrfgBS4Ljc-TEIjMdiekOw/s500/20230304_124549.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="335" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgxADcwMKL4YnHO_cOCqleIaQfbo6p5GZCRRoNx2wVZirs4y9Jj1o4jFPtsORsV79Nu6K_yKLskoPUyT2_VM_oGVwV0kAcFlbWm_TJVq0TGFfscMQW1Xn7e2zWlipDZeKiwOdZpMv_8Ne9_Ur4Pf4m10AIdP5-9OVsrSpSrfgBS4Ljc-TEIjMdiekOw/s16000/20230304_124549.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Tiny ML for use in ecological conservation and gesture recognition<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6k7AoYkCmUxTFNJUeThFcX_54NNio9Kwz4DPzIEpvJLMre9cneD8DCarhMJC0V3NPEHM26FpC-jZsOSBpZvwWneXbctdvLHW29Q-hHJl0iWpGCXl1Vp6krjEVZCMN1plVPi5A38YgHyUfdU-6v1CHhJo-hJPeICWed2GYk8d_NlT2tBj_bCRsbmYz/s400/20230304_124709.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="307" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj6k7AoYkCmUxTFNJUeThFcX_54NNio9Kwz4DPzIEpvJLMre9cneD8DCarhMJC0V3NPEHM26FpC-jZsOSBpZvwWneXbctdvLHW29Q-hHJl0iWpGCXl1Vp6krjEVZCMN1plVPi5A38YgHyUfdU-6v1CHhJo-hJPeICWed2GYk8d_NlT2tBj_bCRsbmYz/s16000/20230304_124709.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Industrial control systems and security<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_O1ZC0BQrS1j46TU54t-j1qvTx0j71oIppXHLJjufmFsRaRlUWEtzXmU8T9zPFs7j47IKmXLjYNAxz4lfFfywuASxmHqEHzoilbd6jlzlmU8OaWInPLxWIWeovjuI0SRXaBDxIJAHXNYzwW_1jnVc3Dj2JtKJ1H03l6-DZ3tjdbdWgvxBCnh9QSuC/s500/20230304_130242.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="336" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_O1ZC0BQrS1j46TU54t-j1qvTx0j71oIppXHLJjufmFsRaRlUWEtzXmU8T9zPFs7j47IKmXLjYNAxz4lfFfywuASxmHqEHzoilbd6jlzlmU8OaWInPLxWIWeovjuI0SRXaBDxIJAHXNYzwW_1jnVc3Dj2JtKJ1H03l6-DZ3tjdbdWgvxBCnh9QSuC/s16000/20230304_130242.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Seeds encased in manure and mud, forming a ball which can be thrown in areas that need aforestation<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgySAh0lqNQLWZjAI1OVd3_3F1hjPYd5xeBBdw1wiVG1aDJ-vTG2cWZL3W0eGqe6Iy6U8K2t80iz7Xq1FoT-WfztKnjT8a-tcuxQBfPJuUOM54_PdFRBpP24UXBZzRTpytKzDtcJuVRayW8B0WnJRrE8Kz5OZpcGUWm_xA2k7oZ7o3OY94R9xmpeCCf/s400/20230304_131719.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="235" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgySAh0lqNQLWZjAI1OVd3_3F1hjPYd5xeBBdw1wiVG1aDJ-vTG2cWZL3W0eGqe6Iy6U8K2t80iz7Xq1FoT-WfztKnjT8a-tcuxQBfPJuUOM54_PdFRBpP24UXBZzRTpytKzDtcJuVRayW8B0WnJRrE8Kz5OZpcGUWm_xA2k7oZ7o3OY94R9xmpeCCf/s16000/20230304_131719.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A travelling salesman game<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5gTqU_BXqmM_YM0qsQAkpVaZU5jDtdYX85tEHBgrQJyzy3oJ1e3_Y-5AIdPcq0PrpImVXPyT1m-qSsz4hd7DInZfplkM816UqWp_Cz3w2tO7qS4MX6vVVtC9-bU7HoR7F9ByW2lcglG3MLYlPLazYhod-0lfbIZdtqgnSXDaRiIF7hxxpNNyyjLi5/s400/20230304_135505.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="280" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh5gTqU_BXqmM_YM0qsQAkpVaZU5jDtdYX85tEHBgrQJyzy3oJ1e3_Y-5AIdPcq0PrpImVXPyT1m-qSsz4hd7DInZfplkM816UqWp_Cz3w2tO7qS4MX6vVVtC9-bU7HoR7F9ByW2lcglG3MLYlPLazYhod-0lfbIZdtqgnSXDaRiIF7hxxpNNyyjLi5/s16000/20230304_135505.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Ice cream made from milk cream, milk, vanilla essence and frozen with liquid nitrogen. They give you samples to taste, and it's delicious!<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy5f7w9yNdFUiu2dN1QbUDsfclT1UChkouyvRvMMcj7xEG44lMJeLZWlP32Hjw6ipvEMdWkEmCzDYoMogwwLrffdZ75mUOm6jSLiW8mXTLLt-p63rKFXR0RGoNrv8ANo9vOtL-aQry4oGzy4XHBwTLJMldWp7K7fuGCS7iGAJ6tmzPvdHePu-HCCFO/s400/20230304_140546.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="211" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgy5f7w9yNdFUiu2dN1QbUDsfclT1UChkouyvRvMMcj7xEG44lMJeLZWlP32Hjw6ipvEMdWkEmCzDYoMogwwLrffdZ75mUOm6jSLiW8mXTLLt-p63rKFXR0RGoNrv8ANo9vOtL-aQry4oGzy4XHBwTLJMldWp7K7fuGCS7iGAJ6tmzPvdHePu-HCCFO/s16000/20230304_140546.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Flexible solar panels<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiC8QrBQ5SXEMFwoOXt10AqZtdDgZY6JXJEpVotZ91k9L7NJoAfZzLFALYtiVMQYTf5CItUIJmXuluDt1YyZ9z1qUSJwRSKke0aEaLQX0FeUZRD8xww1yj-336DSYqS7bZp2HHZXK5Q-VlnqH8groJFdUY1MPTCBPBge-TZWczFT4Kkp1dH012XGD5/s500/20230304_141411.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="258" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiiC8QrBQ5SXEMFwoOXt10AqZtdDgZY6JXJEpVotZ91k9L7NJoAfZzLFALYtiVMQYTf5CItUIJmXuluDt1YyZ9z1qUSJwRSKke0aEaLQX0FeUZRD8xww1yj-336DSYqS7bZp2HHZXK5Q-VlnqH8groJFdUY1MPTCBPBge-TZWczFT4Kkp1dH012XGD5/s16000/20230304_141411.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Toys that make learning mathematics fun<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYRXRkT6b5iYetxbBnOprTWP2cEZfisG6NI5EC6F3vfnUcRMe5Z8cpzVi0b_XlVPdS8LuA-XuBHxyP6jIKR8-qIHcbM_MvvD0DOH2TMALdFdQ1Fy4AuY3iVmcZwhWlBx71T4EHQln2x6OqWnBLGDodWcPECLnmypBC65AIwhmuT_H-hru7tCEUK-3o/s400/20230304_141442.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="339" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYRXRkT6b5iYetxbBnOprTWP2cEZfisG6NI5EC6F3vfnUcRMe5Z8cpzVi0b_XlVPdS8LuA-XuBHxyP6jIKR8-qIHcbM_MvvD0DOH2TMALdFdQ1Fy4AuY3iVmcZwhWlBx71T4EHQln2x6OqWnBLGDodWcPECLnmypBC65AIwhmuT_H-hru7tCEUK-3o/s16000/20230304_141442.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Rainwater harvesting model<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGcR1GN_77MFzmjAojsY0tSMUHHXCicv3Rp9wZExWhWhi9Pl084wrN5yiRlo89Pn8X6vLCrY2QiemLRSqOpxgqPg3yQB7wvKSt_XXUIcbokFX5dgPfYLz8AqY1tmSsBZ43rabwWm6uqN7dXwgxD6zs9Ay9JmCAzG9KuRdGAF4Q4CbtBLCjFknuVa7u/s300/20230304_141606.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="270" data-original-width="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGcR1GN_77MFzmjAojsY0tSMUHHXCicv3Rp9wZExWhWhi9Pl084wrN5yiRlo89Pn8X6vLCrY2QiemLRSqOpxgqPg3yQB7wvKSt_XXUIcbokFX5dgPfYLz8AqY1tmSsBZ43rabwWm6uqN7dXwgxD6zs9Ay9JmCAzG9KuRdGAF4Q4CbtBLCjFknuVa7u/s16000/20230304_141606.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Tender coconuts at an outrageous price of Rs.40<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi2e9MaLDSGDpcPOwTEy3uotHZceic_EW7KE4lcSedqTDPAyEj--iuq53D5QtEDytnILwWZmC7x9-5o5nsXY08geBS0RdOJr929xVMLDlqBhp-p8dHgakINw8ZIdufcJZZnKdz6ZTlQxL4KKuuEDLvJbZ5N8rzi_6lyjoqGPCRvRKbab7i0UArvlU1p/s400/20230304_142932.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="378" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi2e9MaLDSGDpcPOwTEy3uotHZceic_EW7KE4lcSedqTDPAyEj--iuq53D5QtEDytnILwWZmC7x9-5o5nsXY08geBS0RdOJr929xVMLDlqBhp-p8dHgakINw8ZIdufcJZZnKdz6ZTlQxL4KKuuEDLvJbZ5N8rzi_6lyjoqGPCRvRKbab7i0UArvlU1p/s16000/20230304_142932.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A bus driving simulator you can try. The simulator is actually used to train BMTC drivers to handle Indian roads. Surprisingly, there were no potholes on the road. So much for trying to simulate "Indian" roads. <br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLBPTptFJrCQ93u2nxX1wkTb6g9Z-xb8dTQnSEyIhjxJKVJAvWC-dhWyuDXos1ffU_fq1g64p_NMEakoq81HjlaYOXgN_B4vDyJsOOl1WXLK0o1mggSLQfrm320Hu-5YwQbmAUlK81lHFbL_-vOb82emND3nX-010AingswbHsQq4e-J9bP47u4iAW/s329/20230304_144547.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="329" data-original-width="300" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgLBPTptFJrCQ93u2nxX1wkTb6g9Z-xb8dTQnSEyIhjxJKVJAvWC-dhWyuDXos1ffU_fq1g64p_NMEakoq81HjlaYOXgN_B4vDyJsOOl1WXLK0o1mggSLQfrm320Hu-5YwQbmAUlK81lHFbL_-vOb82emND3nX-010AingswbHsQq4e-J9bP47u4iAW/w584-h640/20230304_144547.jpg" width="584" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A toll-gate game where they design the game to see if you choose to use the toll and then switch to a non-toll road and then switch back to the toll road based on traffic. This game had a lottery for players, based on how often they used the toll road.<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJTzs4RoR_8jWm-9RFtAfl41yeB3ecS-omSIdlH_ybKd41528GYF08zfO9kuo3_PqVykOKwUd9hlqxnqnAgvdvAm3F0uMkaaBU7AmcTG50KerRhAZsSzB2XjMZyy0EOr3owTW_-KosK2HfCWW0aLUXulf-6cxysnavjc4yGq0f4sPbvcFHHO0Pgi_a/s300/20230304_150716.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="236" data-original-width="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJTzs4RoR_8jWm-9RFtAfl41yeB3ecS-omSIdlH_ybKd41528GYF08zfO9kuo3_PqVykOKwUd9hlqxnqnAgvdvAm3F0uMkaaBU7AmcTG50KerRhAZsSzB2XjMZyy0EOr3owTW_-KosK2HfCWW0aLUXulf-6cxysnavjc4yGq0f4sPbvcFHHO0Pgi_a/s16000/20230304_150716.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A traffic junction game where you control the signals. The player who reaches the highest level wins a pen<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmwAKPSGNzKvo5j09rRXFexeB7-ExSw_BP3ckiJojrqsEMWtEQVs-9m5g_NiNWTVx47UpBO1NcQw2FNoXdwnJhYasdzT5fwy2-LGbgLu0z3aNc0jvxKRo9o2rNbCiXZYaMKZv3F0Z3qcbIE5p1xaMLZ67PKJ1egdMiMf673wz3FZQ9br1dWZKRC4Ny/s400/20230304_152227.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="206" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmwAKPSGNzKvo5j09rRXFexeB7-ExSw_BP3ckiJojrqsEMWtEQVs-9m5g_NiNWTVx47UpBO1NcQw2FNoXdwnJhYasdzT5fwy2-LGbgLu0z3aNc0jvxKRo9o2rNbCiXZYaMKZv3F0Z3qcbIE5p1xaMLZ67PKJ1egdMiMf673wz3FZQ9br1dWZKRC4Ny/s16000/20230304_152227.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Robot football<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5Yl76A_49wNQzxCva5cc_4-4hmXsFDTTQpH6HAO0HSnNGO4xbcYDMTCCVPkDaSlFq5XtI0cQ2BHM4lpMoq77VNjUU9XYBO4EF6ZuEqrw4fAUwD_UFr0io2Nu8VSXNJOaEf5qgco7sO2qZHIotob8B_kqQjVXdqT4tw8Pelx6nHFInQ8DRvX8RU8iN/s400/20230304_152402.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="321" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5Yl76A_49wNQzxCva5cc_4-4hmXsFDTTQpH6HAO0HSnNGO4xbcYDMTCCVPkDaSlFq5XtI0cQ2BHM4lpMoq77VNjUU9XYBO4EF6ZuEqrw4fAUwD_UFr0io2Nu8VSXNJOaEf5qgco7sO2qZHIotob8B_kqQjVXdqT4tw8Pelx6nHFInQ8DRvX8RU8iN/s16000/20230304_152402.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">A robot kit for children to create their own robots<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiaTrm9zwxOqH857EKtQvdPw7T-2qQrf4YjBx77138V5T3SaIhi1BIg6CDWVWx5tLR44S19H37ZkBJEWvdeJFHghGkWBYonEGcCCYxMPuMzmmj1MLljR0AJkmBAyupCR0B4d91DVE5cGyLDKoqK7uF1b282wDD0KprcdUfBJ13NiQz0mHLi3mkdNT36/s500/20230304_154320.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="392" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiaTrm9zwxOqH857EKtQvdPw7T-2qQrf4YjBx77138V5T3SaIhi1BIg6CDWVWx5tLR44S19H37ZkBJEWvdeJFHghGkWBYonEGcCCYxMPuMzmmj1MLljR0AJkmBAyupCR0B4d91DVE5cGyLDKoqK7uF1b282wDD0KprcdUfBJ13NiQz0mHLi3mkdNT36/s16000/20230304_154320.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The high voltage lab setup<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTf9T1mbJvJUpEEbqk_IzZuVtdFJfkzzi6yo13zWuKu-i7xkKRFPS7WiUDWDqNrqg9Fe1By2WOMXyLV_Gu7iamXFEKF0-NSmSut_dQcN8KRgC2iFoWU5wgHApy1p01VUJbfC7DznPm4J_hFr04RMChckQaAPCturaq7ANClHNMDhKqzOdpNHg6iX87/s400/20230304_154859.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="330" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgTf9T1mbJvJUpEEbqk_IzZuVtdFJfkzzi6yo13zWuKu-i7xkKRFPS7WiUDWDqNrqg9Fe1By2WOMXyLV_Gu7iamXFEKF0-NSmSut_dQcN8KRgC2iFoWU5wgHApy1p01VUJbfC7DznPm4J_hFr04RMChckQaAPCturaq7ANClHNMDhKqzOdpNHg6iX87/s16000/20230304_154859.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Testing aircraft for lightning strikes<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKQ2h3oTRs4RZLTNeEYeeqeWSvGTfF9vCidQ_SzHGokr8cci5kis6fpUyJBWFsuaPGrCOlFbbxThFJJ2q2Fszx5_m3XchwXHY8IlPamRdFmdIYhIzDpGf9OAd8PVEYPsQCyhGlow00l_6S9qQJhWKJHKwaqYF6g3jALeQbnnL3eCMH0HRcK-5FuSHC/s600/20230304_154933.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="352" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjKQ2h3oTRs4RZLTNeEYeeqeWSvGTfF9vCidQ_SzHGokr8cci5kis6fpUyJBWFsuaPGrCOlFbbxThFJJ2q2Fszx5_m3XchwXHY8IlPamRdFmdIYhIzDpGf9OAd8PVEYPsQCyhGlow00l_6S9qQJhWKJHKwaqYF6g3jALeQbnnL3eCMH0HRcK-5FuSHC/s16000/20230304_154933.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">More on the lightning strikes<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBE2NyvLNmKBJO0EobWTtqzkBgcw9v4rjDhUpYsRASUzMXaAC5N-70ZbdQ13kv0JWKzZST12b0jhVQgsRm7P67QGbtEWh3r43wH1McfIhPPNSWmQs3ymN-w5nrxyPkg6mD5CnxNT5spdWsrtyIApy9SeOb1mx3msvRK6y9vgYLsZvvQKMCY4_-X-Yh/s600/20230304_155003.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="472" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBE2NyvLNmKBJO0EobWTtqzkBgcw9v4rjDhUpYsRASUzMXaAC5N-70ZbdQ13kv0JWKzZST12b0jhVQgsRm7P67QGbtEWh3r43wH1McfIhPPNSWmQs3ymN-w5nrxyPkg6mD5CnxNT5spdWsrtyIApy9SeOb1mx3msvRK6y9vgYLsZvvQKMCY4_-X-Yh/s16000/20230304_155003.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The demos shown at the high voltage lab. The queue is quite large here<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgXXarkcS95nfRu-1mAoLKAIo3cR1sD7FI2XTUeIJBXFYmsALzMLPgNtGD9B8ebCnhT9Sa0ENTdKT5YJAthvNkr6FgzvjMYQ6iXzNDFgexR5inVUy4CxNuhvm9UGHw8XQE4yO89QVSxyxXMmrv4NzGzsbQ-iwWkJ1FUGVNnxJR-uot48OmFDJ0kwqmr/s400/20230304_155103.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="273" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgXXarkcS95nfRu-1mAoLKAIo3cR1sD7FI2XTUeIJBXFYmsALzMLPgNtGD9B8ebCnhT9Sa0ENTdKT5YJAthvNkr6FgzvjMYQ6iXzNDFgexR5inVUy4CxNuhvm9UGHw8XQE4yO89QVSxyxXMmrv4NzGzsbQ-iwWkJ1FUGVNnxJR-uot48OmFDJ0kwqmr/s16000/20230304_155103.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">The brain research centre<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8A-INRaN-iAtjzquRUiaSIYOAfHRZvDzfQT3rBJ23HOhyxNSJT2Hg6GJZCsRcyL2SjlaCPw01-OG_-z3Tv0c79Wv7CjdMPswVpf_torGwdASoOI6tWLE7c-S9F2DuwwV1ike_ABLQlfOyO8UdsG1Tq0OoRuF46aQTOaQU8uXHZmwrXHVu5MYjXX7g/s583/20230304_155738.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="388" data-original-width="583" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8A-INRaN-iAtjzquRUiaSIYOAfHRZvDzfQT3rBJ23HOhyxNSJT2Hg6GJZCsRcyL2SjlaCPw01-OG_-z3Tv0c79Wv7CjdMPswVpf_torGwdASoOI6tWLE7c-S9F2DuwwV1ike_ABLQlfOyO8UdsG1Tq0OoRuF46aQTOaQU8uXHZmwrXHVu5MYjXX7g/s16000/20230304_155738.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">If you ever wondered how many idlis you need. Apparently, idlis contain protein too.<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrhqUcGefW6yXZ0fUXYHviWWO8fkrtDVm52QNihZq61ng0TcMK3ldZzerx5IsnK0kP99z9tb_kdNE5-e7OsBjUYdKW3vLzlvT88pOn11w0QGR2EHos0PYF-k7RFfKCc_TX8rH3Db6pLKC7v45AkRssX2QBtAUBKec4P0FcAHtxoWeuFv0mtfqHvSPz/s1024/20230304_161147.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="789" data-original-width="1024" height="494" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrhqUcGefW6yXZ0fUXYHviWWO8fkrtDVm52QNihZq61ng0TcMK3ldZzerx5IsnK0kP99z9tb_kdNE5-e7OsBjUYdKW3vLzlvT88pOn11w0QGR2EHos0PYF-k7RFfKCc_TX8rH3Db6pLKC7v45AkRssX2QBtAUBKec4P0FcAHtxoWeuFv0mtfqHvSPz/w640-h494/20230304_161147.jpg" width="640" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Click to view bigger<br /></td></tr></tbody></table><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_EZLcLkP73VtZk4m8YPdb0hErlNUFRGOgV4sIqaT4zGBZbA0s8NvgWFHVe32mWTwxKF98ahhHfePt_iPJbgfP1aQseSsFq_4bw03Ci5CfVQWlO5PKzkS-qxjV6J5IS9zeWDCMQugZFMjXd1kVhUoNxIzKJDv-acEg22s_F8UQ6KoX371aGFk3Bq2E/s400/20230304_162302.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="348" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_EZLcLkP73VtZk4m8YPdb0hErlNUFRGOgV4sIqaT4zGBZbA0s8NvgWFHVe32mWTwxKF98ahhHfePt_iPJbgfP1aQseSsFq_4bw03Ci5CfVQWlO5PKzkS-qxjV6J5IS9zeWDCMQugZFMjXd1kVhUoNxIzKJDv-acEg22s_F8UQ6KoX371aGFk3Bq2E/s16000/20230304_162302.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Explanation of flash floods<br /></td></tr></tbody></table><br /><br /><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhE6TtmnJyfbF_b3HxImpo-iLAhCbNaptTAiTOK1awUi_etcnzmDczMc_wY2WwMwWb3cKWdKai9-2CgBpYZn8NyJXXaQDGj_i4HAsNLvGUm6k9MbGjbs7TuZ33wxSy0MHnl-qHnFCQTTbVThZh02ZBM2DB4O9qydBjXgRl0vi-c3rhKlArUL9crmqLI/s400/trainHydrogen.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="242" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhE6TtmnJyfbF_b3HxImpo-iLAhCbNaptTAiTOK1awUi_etcnzmDczMc_wY2WwMwWb3cKWdKai9-2CgBpYZn8NyJXXaQDGj_i4HAsNLvGUm6k9MbGjbs7TuZ33wxSy0MHnl-qHnFCQTTbVThZh02ZBM2DB4O9qydBjXgRl0vi-c3rhKlArUL9crmqLI/s16000/trainHydrogen.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Hydrogen fuel cell powering a toy train<br /></td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhE6TtmnJyfbF_b3HxImpo-iLAhCbNaptTAiTOK1awUi_etcnzmDczMc_wY2WwMwWb3cKWdKai9-2CgBpYZn8NyJXXaQDGj_i4HAsNLvGUm6k9MbGjbs7TuZ33wxSy0MHnl-qHnFCQTTbVThZh02ZBM2DB4O9qydBjXgRl0vi-c3rhKlArUL9crmqLI/s400/trainHydrogen.jpg" style="margin-left: 1em; margin-right: 1em;"></a></div></div><br /><div class="separator" style="clear: both; text-align: center;"></div><div class="separator" style="clear: both; text-align: center;"></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img border="0" data-original-height="225" data-original-width="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPsRSTcYXQfWH3bDj8FJnnaxhkuz9IlP1ezTQiymju8ItGj2eHiO89phsmUxNLeRq2MEfiH7Fp2LgJv0TC27tJYF3YWC3y5fqrTMhPoNXuk9fwsqnjX-H5Hy8ZZitlNI33vZQLzm_zVLzoxBOcVV49VtppFKbVQ3p1MO4t0Rn0c59GkNwnUOYzCDmB/s16000/tactileSystems.jpg" style="margin-left: auto; margin-right: auto;" /></td></tr><tr><td class="tr-caption" style="text-align: center;">Tactile robot arms<br /></td></tr></tbody></table><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhL7TJTEwiqyGkD8pH0lmPd_ZJH0KmhG0gxkRZkSInRLxZ8hzYkCilMdAC-Ux1v_Xe13z-xS9TPSU4T44tc3xo0Ror7J6N8WNKi1Er91pMCoKR_b0Z-KAosONTIl0p5FD3WI6nXyevptans8VGIfZ8c9X_YMXX9wILxv7wfN2RWErGhIjvegS0tuKxc/s600/20230304_164612.jpg" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="427" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhL7TJTEwiqyGkD8pH0lmPd_ZJH0KmhG0gxkRZkSInRLxZ8hzYkCilMdAC-Ux1v_Xe13z-xS9TPSU4T44tc3xo0Ror7J6N8WNKi1Er91pMCoKR_b0Z-KAosONTIl0p5FD3WI6nXyevptans8VGIfZ8c9X_YMXX9wILxv7wfN2RWErGhIjvegS0tuKxc/s16000/20230304_164612.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Some people travelled all the way from Kerala just for Open Day<br /></td></tr></tbody></table><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPsRSTcYXQfWH3bDj8FJnnaxhkuz9IlP1ezTQiymju8ItGj2eHiO89phsmUxNLeRq2MEfiH7Fp2LgJv0TC27tJYF3YWC3y5fqrTMhPoNXuk9fwsqnjX-H5Hy8ZZitlNI33vZQLzm_zVLzoxBOcVV49VtppFKbVQ3p1MO4t0Rn0c59GkNwnUOYzCDmB/s400/tactileSystems.jpg" style="margin-left: 1em; margin-right: 1em;"></a></div><p></p><p>What's shown here is a minuscule subset of the massive number of exhibits and interactive games that IISc had organized. Kids can win chocolates and prizes too in the games organized. One day isn't enough to go through all the exhibits. There are so many.</p><p>Parking is available at IISc Gymkhana ground for free. However, be prepared to end up with a dusty vehicle, and if you don't find a shady area, your vehicle gets baked in the afternoon sun.</p><p>Each of the departments have toilets that you can use. There are plenty of food stalls available, but it's better if you bring sandwiches on your own, since the stalls are overpriced. There are plenty of places on the lush green campus where you can sit and relax. Everyone is helpful. <br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-77134462082525856032023-02-04T10:48:00.000+05:302023-02-04T10:48:00.187+05:30Real path to linked files<p>In Ubuntu, when you click on a link to a folder and reach that folder via Nautilus, you are only shown the path you came from, rather than the actual path of the folder or file. </p><p>For example, it'd be:</p><p><i>/media/nav/<b>Link_to_folder</b>/folderOrFile</i></p><p>However, the actual path of the file would be:</p><p><i>/media/nav/<b>Transcend_NTFS/NavData/backup</b>/folderOrFile</i></p><p>Even if you open a terminal in the linked folder, a pwd command would still show you only the linked path, rather than the actual path.</p><p>To view the actual path, simply use:</p><p><i><b>realpath fileOrFolderName</b></i><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-5432189324810023172023-01-15T17:27:00.004+05:302023-10-11T17:25:35.555+05:30Comparing video quality by viewing videos side by side<p>When you convert videos to a different quality, you'd want to compare them. While there are <a href="https://pypi.org/project/ffmpeg-quality-metrics/" target="_blank">objective methods</a> of comparing it, the subjective evaluation by a human is generally more accurate. When looking for solutions, I found a brilliant solution named <a href="https://github.com/vivictorg/vivict" target="_blank">Vivict</a>. It not only allows you to compare streaming videos, it also allows you to compare local videos, by using the little white icon near the address bar. The best part of Vivict, is that it shows half of one video and half of another, and allows you to move the mouse to view more or less of the video.</p><p><b>Limitation of Vivict:</b> It does not support all video file formats. When I wanted to play an mkv file, it didn't work. <br /></p><p>Another solution, is to simply <a href="https://stackoverflow.com/questions/37145580/how-to-asymmetrical-video-side-by-side" target="_blank">use ffplay</a> like this:</p><p><span style="font-family: courier;"><i>ffplay -f lavfi "movie=<b>leftVideo.mp4</b>,scale=iw/2:ih[v0];movie=<b>rightVideo.mp4</b>,scale=iw/2:ih[v1];[v0][v1]hstack" </i></span><br /></p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhVT41NG8R2v5hlYvW9_Udr-9_Za8027MnF5hG9DoTdefxb11oL_X2VlCPj7fsbDUEFK-kYWD5gFAnxHUJkgYStjSGGmfRtXxjSJE9Qi15gkVTNO4fEL38rzMK65TTbthj8lzNIKJBqdZDmRgNu06zYd2KfDTKv92JLtiDm-KdvzY8avdKRCc9WxZx/s500/ffplay_video_comparison.png" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="157" data-original-width="500" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhVT41NG8R2v5hlYvW9_Udr-9_Za8027MnF5hG9DoTdefxb11oL_X2VlCPj7fsbDUEFK-kYWD5gFAnxHUJkgYStjSGGmfRtXxjSJE9Qi15gkVTNO4fEL38rzMK65TTbthj8lzNIKJBqdZDmRgNu06zYd2KfDTKv92JLtiDm-KdvzY8avdKRCc9WxZx/s16000/ffplay_video_comparison.png" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Comparing videos using ffplay<br /></td></tr></tbody></table><p></p><h2 style="text-align: left;">A simpler solution:</h2><p><a href="https://github.com/nav9/splitVideoQualityViewer" target="_blank">I created a program similar to Vivict</a> which shows the videos like this:</p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDbDorZg5MMpayqHTUCHqMhoTvTW-3BpVbHU2jRUznx1iFNQIWlDwKCpXRxoffeU7zEb0dkzqn4I-I40VQN_Y7MZmvrP1DFyGRl9Fh-FageYq5sHD5DFYIaCYutvxCkg83pU6cm2scNC9vbV_h4ebVz4-h1HYyHWr2WUh--b_Ovlfob_vReklw6iQa/s1102/verticalSplit.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="651" data-original-width="1102" height="378" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDbDorZg5MMpayqHTUCHqMhoTvTW-3BpVbHU2jRUznx1iFNQIWlDwKCpXRxoffeU7zEb0dkzqn4I-I40VQN_Y7MZmvrP1DFyGRl9Fh-FageYq5sHD5DFYIaCYutvxCkg83pU6cm2scNC9vbV_h4ebVz4-h1HYyHWr2WUh--b_Ovlfob_vReklw6iQa/w640-h378/verticalSplit.png" width="640" /></a></div><p> </p><p>Feel free to either fork and modify the repository or contact me on the repository's discussion tab, to collaborate.<br /></p><p><br /></p>
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</div>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-81501399794208747862023-01-01T21:28:00.001+05:302023-01-01T21:28:00.198+05:30Automatically detecting audio video async and correcting it<p>It's a bit disorienting when in you see a lack of sync in the lip movement of a person and the audio. There are various reasons this can happen. If on a live stream, it could be because of network congestion or because the program doing the video encoding didn't get enough of CPU cycles. It can also happen when re-encoding video or splitting it.</p><p>Whether you want to measure how much of an async there is or even to fix it, machine learning has thankfully come to our aid. I created a simplistic open source program that finds periods of time when a person begins to speak after a silence, and matches it with audio to find out how much of an async there is, and then I use FFMPEG to fix the async. </p><p><b>Code:</b> https://github.com/nav9/audio_video_synchronizer<br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgm42TmrFNn8AMGwjkD9jdwWMebgrXnScw9EkPGc0LnFOvg0NivV1f492iFopJmYQNrplvzPgBqNwVENPHCelJ7Z0t7JsD5Ts9tEBhDe0jW-LYTvcBl6tD1G5UHMZ2pFZ2bGyScK-aMTAb9MUNeuVbd3HzWVajAmsJqs4GjFXS1l0c38C9ftFFmXPPq/s394/faceMesh.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="227" data-original-width="394" height="184" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgm42TmrFNn8AMGwjkD9jdwWMebgrXnScw9EkPGc0LnFOvg0NivV1f492iFopJmYQNrplvzPgBqNwVENPHCelJ7Z0t7JsD5Ts9tEBhDe0jW-LYTvcBl6tD1G5UHMZ2pFZ2bGyScK-aMTAb9MUNeuVbd3HzWVajAmsJqs4GjFXS1l0c38C9ftFFmXPPq/s320/faceMesh.png" width="320" /></a></div><p></p><p><a href="https://google.github.io/mediapipe/" target="_blank">MediaPipe</a> was used to identify the lip movements. I was surprised at how robust it is. Even if the person's face is turned sideways, it still tracks and estimates the face point positions as x,y,z points. Almost 400 points are tracked.</p><p>To identify human speech in the audio, I used a voice activity detection algorithm.</p><p>There's a lot more work to be done to make this program generic. Ideally, a Recurrent Neural Network (perhaps even LSTM) could be used to analyze lip movements temporally and figure out which lip movements are actually speech and which aren't. Then a probability score needs to be generated based on the actual sounds detected and matched with the lip movements. There are pre-trained models like <a href="https://alphacephei.com/vosk/models" target="_blank">Vosk</a>, which can be used offline to estimate which words are spoken. Python's <a href="https://pypi.org/project/SpeechRecognition/" target="_blank">speech_recognition</a> package comes in handy here.<br /></p><p><br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0tag:blogger.com,1999:blog-6915405415003666702.post-80259769393052224642022-12-07T22:15:00.001+05:302022-12-07T22:15:00.206+05:30Add shutdown and reboot menu options to GRUB<p>I have ALWAYS wished for this, and can't believe it was only now that a lightbulb lit up in my head. There are plenty of times you'd be at the GRUB (Grand Unified Bootloader) menu, and wish you could simply shutdown from right there, instead of pressing the power button or booting into the operating system and then shutting down. </p><p>This should have been a default option in GRUB. Anyway, here's how you add it:</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHg2kShe3F5SiXWy2iFnxqPSdiHYHW9U_IwTspunwByFIV2uAczaQIKt_vbo27-gHPG4KwGfn6gxR2UnSvrifZNSwnoWnr84sBTrrkqHZodSiVMPW-WIFBv0e2_IuNkngCf6NjxJRWbzuoWaJXnCkLnr-RqFOmWl_U4VZuAlRsfKTHAMBApI8eDx4f/s300/grubWithRebootAndShutdown.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="164" data-original-width="300" height="164" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiHg2kShe3F5SiXWy2iFnxqPSdiHYHW9U_IwTspunwByFIV2uAczaQIKt_vbo27-gHPG4KwGfn6gxR2UnSvrifZNSwnoWnr84sBTrrkqHZodSiVMPW-WIFBv0e2_IuNkngCf6NjxJRWbzuoWaJXnCkLnr-RqFOmWl_U4VZuAlRsfKTHAMBApI8eDx4f/s1600/grubWithRebootAndShutdown.jpg" width="300" /></a></div><p>I saw this technique <a href="https://daulton.ca/2018/08/reboot-and-shutdown-options-grub/" target="_blank">here</a>, and this is why I like Linux more than Windows or Mac. There are tons of things you can customize.</p><p><br /></p><p>This file allows you to add custom menu options to GRUB:</p><p><i><b><span style="font-family: courier;"> sudo gedit /etc/grub.d/40_custom</span></b></i></p><p>Add these options to the end of the file, save and exit:</p><p><i><b><span style="font-family: courier;">menuentry "Reboot" {<br /> reboot<br />}<br /><br />menuentry "Shut Down" {<br /> halt<br />}</span></b></i></p><p>Update GRUB:</p><p><i><b><span style="font-family: courier;">sudo update-grub</span></b></i></p><p>Reboot the computer.<br /></p>Navhttp://www.blogger.com/profile/01320006785676088076noreply@blogger.com0