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Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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Find agent skills by outcome
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Copyright (c) Microsoft Corporation.
repos:
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auto_eval/evaluator_config.json
!sample/*.csv
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*.sh text eol=lf
generic skill
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JARVIS
[](https://arxiv.org/abs/2303.17580)
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TODO: The maintainer of this repo has not yet edited this file
**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
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<!-- BEGIN MICROSOFT SECURITY.MD V0.0.8 BLOCK -->
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet
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Microsoft Open Source Code of Conduct
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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To contribute to this GitHub project, you can follow these steps:
1. Fork the repository you want to contribute to by clicking the "Fork" button on the project page.
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ALL
*.dev.yaml
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Reports
reports/
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[submodule "reasoning-from-scratch"]
path = reasoning-from-scratch
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Build a Large Language Model (From Scratch)
This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book [Build a Large Language Model (From Scratch)](https://amzn.to/4fqvn0D).
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nanoGPT
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.DS_Store
.idea
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Override jupyter in Github language stats for more accurate estimate of repo code languages
*.ipynb linguist-generated
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llm.c
LLMs in simple, pure C/CUDA with no need for 245MB of PyTorch or 107MB of cPython. Current focus is on pretraining, in particular reproducing the [GPT-2](https://github.com/openai/gpt-2) and [GPT-3](https://arxiv.org/abs/2005.14165) miniseries, along with a parallel PyTorch reference implementation
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dot files and such
.vscode
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<div align="center">
<img src="img/banner.png" alt="LLM Course">
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LLMSurvey
> A collection of papers and resources related to Large Language Models.
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<h1 align="center">The Practical Guides for Large Language Models </h1>
<p align="center">
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Byte-compiled / optimized / DLL files
__pycache__/
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Open LLMs
These LLMs (Large Language Models) are all licensed for commercial use (e.g., Apache 2.0, MIT, OpenRAIL-M). Contributions welcome!
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[🏠Home](README.md)
- Want to know which one is "the best"? Have a look at the [🏆 Leaderboards](llm-tools.md#benchmarking) in the Benchmarking section.
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