Why it matters
- YAML-based configuration makes fine-tuning accessible to ML practitioners who don't want to write custom training loops.
- Comprehensive training method support (LoRA, QLoRA, full, RLHF, DPO) in one framework covers the full fine-tuning spectrum.
- Community-built on community knowledge — OpenAccess AI Collective incorporates best practices from the open-source AI community.
- Widely used for producing popular open-source fine-tuned models — many HuggingFace Hub models were trained with Axolotl.
Key capabilities
- Training methods: LoRA, QLoRA, full fine-tuning, RLHF, DPO, ORPO, ReLoRA.
- Model support: Llama 2/3, Mistral, Mixtral, Falcon, Phi, Gemma, MPT, and more.
- YAML configuration: Define the entire training run in a simple, readable YAML file.
- Multi-GPU: FSDP and DeepSpeed integration for distributed training.
- Dataset formats: Alpaca, ShareGPT, Completion, Instruction, and custom formats.
- Flash Attention 2: Automatic integration for memory-efficient training.
- Gradient checkpointing: Reduce memory usage for larger batch sizes.
- W&B integration: Weights & Biases logging for training metrics tracking.
Technical notes
- License: Apache 2.0 (open source)
- GitHub: github.com/OpenAccess-AI-Collective/axolotl (8K+ stars)
- Install:
pip install axolotlor Docker image - GPU: NVIDIA (CUDA required); multi-GPU via FSDP/DeepSpeed
- Python: 3.9+
- Models: Llama 2/3, Mistral, Mixtral, Gemma, Phi-3, Falcon, MPT, and more
- Dataset formats: Alpaca, ShareGPT, completion, custom JSON/JSONL
Ideal for
- ML practitioners who want to fine-tune LLMs on custom datasets without writing training code from scratch.
- Researchers experimenting with different training methods (LoRA vs. DPO vs. RLHF) on the same base model.
- Teams producing specialized models (domain-specific, instruction-tuned, preference-aligned) for open-source release or internal use.
Not ideal for
- Teams without GPU access — Axolotl requires CUDA GPUs; use Predibase for managed training.
- Maximum training speed — Unsloth's custom CUDA kernels are significantly faster.
- Non-technical users who need a UI — Axolotl is CLI-based; Predibase or Together AI have GUIs.