Full Training Frameworks

Fully open-source platform for the complete foundation model lifecycle - from data preparation and training to evaluation and deployment. Supports 100+ models with 200+ recipes for fine-tuning gpt-oss, Qwen3, DeepSeek-R1, and more. Apache 2.0 licensed.

Open-source framework for foundation-model development with composable data, training, and evaluation pipelines for modern LLM research.

One-stop unified framework for SFT, DPO, ORPO, KTO with web UI.

YAML-driven full pipeline for SFT, DPO, GRPO.

Unified training framework for 600+ LLMs and 300+ MLLMs with CPT/SFT/DPO/GRPO (AAAI 2025).

2× faster, 70% less memory fine-tuning.

Clean from-scratch implementations of 20+ LLMs.

PyTorch-native library for post-training, fine-tuning, and experimentation with LLMs.

Gradio-based GUI and CLI for training Stable Diffusion models (LoRA, Dreambooth, fine-tuning, SDXL). Provides accessible interface to Kohya's powerful training scripts.

Official library for RLHF, SFT, DPO, ORPO.

Volcano Engine Reinforcement Learning for LLMs with PPO, GRPO, REINFORCE++, DAPO (EuroSys 2025).

Scalable toolkit for efficient model reinforcement with DTensor and Megatron backends.

Easy-to-use, scalable RLHF framework based on Ray. Supports PPO, GRPO, REINFORCE++, DAPO with vLLM integration and async training. Apache 2.0 licensed.

Extensible toolkit for finetuning and inference of large foundation models. Features RAFT alignment algorithm and comprehensive model support. Apache 2.0 licensed.

A next-generation training engine built for ultra-large MoE models with efficient QLoRA and full-parameter fine-tuning. Apache 2.0 licensed.

Low-code framework for building custom LLMs and deep neural networks. Declarative YAML configuration for training state-of-the-art models with PEFT/LoRA, 4-bit quantization, distributed training via Hugging Face Accelerate, and native Kubernetes support. Linux Foundation AI project. Apache 2.0 licensed.

PyTorch native platform for training generative AI models at scale. Showcases 4D parallelism (FSDP, tensor, pipeline, context) for LLM pretraining with 65%+ speedups over optimized baselines. BSD-3-Clause licensed.

Versatile framework for both single- and multi-modal pre-training and post-training. Model-centric distributed recipe zoo supporting text, vision, audio, and video models with unified training interface. Apache 2.0 licensed.

No-code GUI framework for fine-tuning LLMs. Streamlined interface for SFT, reward modeling, and model deployment. Apache 2.0 licensed.

Agentic RL Training at Scale from Prime Intellect. Framework for large-scale reinforcement learning capable of scaling to 1000+ GPUs with fully asynchronous RL, FSDP2 training, and vLLM inference. Apache 2.0 licensed.

LLM post-training framework for RL Scaling from THUDM. Supports SFT and RL training with multi-turn compilation feedback, powering projects like TritonForge for automated GPU kernel generation. Apache 2.0 licensed.

Democratizing Reinforcement Learning for LLMs. Framework for training AI agents with RL featuring near-zero code changes, CLI-first workflow, and 50+ built-in benchmarks. Supports GRPO, REINFORCE, RLOO with verl and tinker backends. Apache 2.0 licensed.

Efficient, scalable, multi-modality RL training framework based on veRL. Extends veRL to support vision-language models with GRPO algorithm for efficient RL training. Apache 2.0 licensed.

Making AI for robotics more accessible with end-to-end learning. State-of-the-art approaches for imitation learning and reinforcement learning with pretrained models, datasets, and simulated environments. Apache 2.0 licensed.