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LLM 🏁
Code and documentation to train Stanford's Alpaca models, and generate the data.
Locally run an Instruction-Tuned Chat-Style LLM
The simplest way to run LLaMA on your local machine
Making large AI models cheaper, faster and more accessible
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Resource list for generating JSON using LLMs via function calling, tools, CFG. Libraries, Models, Notebooks, etc.
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Port of OpenAI's Whisper model in C/C++
Official supported Python bindings for llama.cpp + gpt4all
Google Research
OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHX…
📋 A list of open LLMs available for commercial use.
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
A guidance language for controlling large language models.
Inference code and configs for the ReplitLM model family
Build, evaluate, understand, and fix LLM-based apps
A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs).
CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.
LongLLaMA is a large language model capable of handling long contexts. It is based on OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.
Run any open-source LLMs, such as Llama, Mistral, as OpenAI compatible API endpoint in the cloud.
FauxPilot - an open-source alternative to GitHub Copilot server
Automatically Discovering Fast Parallelization Strategies for Distributed Deep Neural Network Training
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
A high-throughput and memory-efficient inference and serving engine for LLMs