15 projectsLLM Training Frameworks
A framework that specializes in efficient fine-tuning. On its GitHub page, you can find ready-to-use fine-tuning templates for various LLMs, allowing you to easily train your own data for free on the Google Colab cloud.
PythonApache License 2.0active
Making large AI models cheaper, faster, and more accessible.
PythonApache License 2.0active
Ongoing research training transformer models at scale.
PythonOtheractive
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
PythonApache License 2.0active
Open-source framework for fine-tuning and evaluating LLMs. It simplifies the process of experimenting with different training configurations and makes it easy to reproduce and share results, supporting features like LoRA, QLoRA, DeepSpeed, PEFT, and multi-GPU setups.
PythonApache License 2.0active
An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & RingAttention & RFT).
PythonApache License 2.0active
An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
PythonApache License 2.0active
A native PyTorch Library for large model training.
PythonBSD 3-Clause "New" or "Revised" Licenseactive
a lean, efficient, and easy-to-hack codebase to research LLMs.
PythonBSD 3-Clause "New" or "Revised" Licenseslowing
A library for accelerating Transformer model training on NVIDIA GPUs.
PythonApache License 2.0active
An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models.
PythonApache License 2.0active
Minimalistic large language model 3D-parallelism training.
PythonApache License 2.0active
A simple, performant and scalable Jax LLM!
PythonApache License 2.0active
Mesh TensorFlow: Model Parallelism Made Easier.
PythonApache License 2.0slowingarchived
Efficient Training for Big Models.
PythonApache License 2.0active