Find agent skills by outcome

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Ignore login file

*.bin

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Copyright (c) Microsoft Corporation.

repos:

Microsoft Open Source Code of Conduct

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

Contributing

This project welcomes contributions and suggestions. Most contributions require you to

<h1 align="center">

<img src="./.asset/logo.color.svg" width="45" /> TaskWeaver

*.sh text eol=lf

generic skill

auto_eval/evaluator_config.json

!sample/*.csv

JARVIS

[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2303.17580)

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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ALL

*.dev.yaml

Microsoft Open Source Code of Conduct

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

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.

Configs and keys

.chainlit

[submodule "reasoning-from-scratch"]

path = reasoning-from-scratch

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).

.DS_Store

.idea

nanoGPT

![nanoGPT](assets/nanogpt.jpg)

Override jupyter in Github language stats for more accurate estimate of repo code languages

*.ipynb linguist-generated

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