#1010th of 22 in Training and fine-tuning
axolotl
YAML-driven fine-tuning framework for LLMs with LoRA, QLoRA, RL and multi-GPU
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- Stars
- 12.6k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
11,460 on 31 Mar 202612,601 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Axolotl runs LLM post-training from a single YAML config covering dataset preprocessing, training, evaluation, quantization and inference. It supports full fine-tuning, LoRA/QLoRA, QAT, DPO/KTO/ORPO, GRPO and reward modelling, with FSDP2, DeepSpeed, and Torchrun or Ray multi-node. Models come from the Hugging Face Hub, including multimodal and audio ones.
Who it is for: ML engineers fine-tuning open LLMs on their own GPUs or rented cloud
Strengths
- One YAML config reused across preprocessing, training, evaluation, quantization and inference
- Broad methods: full FT, LoRA/QLoRA, QAT, DPO/KTO/ORPO, GRPO, reward models
- Multi-GPU and multi-node via FSDP2, DeepSpeed, Torchrun and Ray, plus context and expert parallelism
- Fast model support updates; GGUF export via `axolotl export` for llama.cpp and Ollama
Weaknesses
- Requires NVIDIA (Ampere or newer for bf16) or AMD GPU; no CPU path stated
- Needs Python 3.12+ and PyTorch 2.13+, a narrow environment
- Configuration surface is large; many options and fast-moving features
- No web UI; command-line and YAML only
What it needs
- GPU required
- Compose
- Needs PyTorch, Hugging Face Hub, DeepSpeed (optional), uv
- Models: Llama, Mistral, Mixtral, GPT-OSS, Qwen
Also in Training and fine-tuning
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|---|---|---|
| 1 | unslothDesktop and web app to run, fine-tune and export open models | 88 out of 100 |
| 2 | deepspeedPyTorch library for distributed training of large models with ZeRO | 83 out of 100 |
| 3 | verlReinforcement learning training library for LLM post-training with PPO and GRPO | 82 out of 100 |
| #4 | peftPython library for LoRA and other parameter-efficient fine-tuning of Transformers models | 81 out of 100 |
| #5 | trlPost-training library with SFT, DPO, GRPO and KTO trainers | 78 out of 100 |