web-template

Author: “skzhang1”

website/README.md

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+# Website
+
+This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
+
+## Prerequisites
+
+To build and test documentation locally, begin by downloading and installing [Node.js](https://nodejs.org/en/download/), and then installing [Yarn](https://classic.yarnpkg.com/en/).
+On Windows, you can install via the npm package manager (npm) which comes bundled with Node.js:
+
+```console
+npm install --global yarn
+```
+
+## Installation
+
+```console
+pip install pydoc-markdown
+cd website
+yarn install
+```
+
+## Local Development
+
+Navigate to the website folder and run:
+
+```console
+pydoc-markdown
+yarn start
+```
+
+This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.

website/babel.config.js

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+module.exports = {
+  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
+};

website/docs/Contribute.md

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+# AutoGen - Automated Multi Agent Chat
+
+`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance via multi-agent conversation.
+Please find documentation about this feature [here](/docs/Use-Cases/Autogen#agents).
+
+Links to notebook examples:
+* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)
+* [Auto Code Generation, Execution, Debugging and Human Feedback](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_human_feedback.ipynb)
+* [Solve Tasks Requiring Web Info](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_web_info.ipynb)
+* [Use Provided Tools as Functions](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_function_call.ipynb)
+* [Automated Task Solving with Coding & Planning Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_planning.ipynb)
+* [Automated Task Solving with GPT-4 + Multiple Human Users](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_two_users.ipynb)
+* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_chess.ipynb)
+* [Automated Task Solving by Group Chat](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
+* [Automated Continual Learning from New Data](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_stream.ipynb)

website/docs/Examples/AutoGen-AgentChat.md

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+# AutoGen - Tune GPT Models
+
+`flaml.autogen` offers a cost-effective hyperparameter optimization technique [EcoOptiGen](https://arxiv.org/abs/2303.04673) for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them.
+Please find documentation about this feature [here](/docs/Use-Cases/Autogen#enhanced-inference).
+
+Links to notebook examples:
+* [Optimize for Code Generation](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_openai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_openai_completion.ipynb)
+* [Optimize for Math](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_chatgpt_gpt4.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_chatgpt_gpt4.ipynb)