474208b794
* fix: run deduplication before saving dataset during preprocessing Move deduplicate_and_log_datasets call before save_preprocessed_dataset in both SFT and RL data loading pipelines. This ensures the saved preprocessed dataset is already de-duplicated, so subsequent loads from cache don't contain duplicates. Fixes #2719 * fix: include deduplication flag in dataset hash and warn on skip_prepare_dataset+dedup - Add dataset_exact_deduplication to the hash string in generate_dataset_hash_from_config so cached datasets are invalidated when the dedup setting changes. - Log a warning when skip_prepare_dataset=True and dataset_exact_deduplication=True, since dedup will be silently skipped in that configuration (both SFT and RL paths). * fix: add ValueError for skip_prepare+dedup, fix test mock target and formatting - Add config validator (check_deduplication_with_skip_prepare) that raises ValueError when skip_prepare_dataset=True and dataset_exact_deduplication=True - Replace runtime warnings in sft.py/rl.py with the validator check - Fix RL test: patch axolotl.utils.data.rl.load_tokenizer instead of axolotl.loaders.load_tokenizer to properly mock the imported reference - Fix ruff lint (remove unused imports) and formatting issues * refactor: inline deduplicate function per review feedback * fix test fixture, lint --------- Co-authored-by: ManasVardhan <manasvardhan@users.noreply.github.com> Co-authored-by: Wing Lian <wing@axolotl.ai>
A Free and Open Source LLM Fine-tuning Framework
🎉 Latest Updates
- 2025/12: Axolotl now includes support for Kimi-Linear, Plano-Orchestrator, MiMo, InternVL 3.5, Olmo3, Trinity, and Ministral3.
- 2025/10: New model support has been added in Axolotl for: Qwen3 Next, Qwen2.5-vl, Qwen3-vl, Qwen3, Qwen3MoE, Granite 4, HunYuan, Magistral 2509, Apertus, and Seed-OSS.
- 2025/09: Axolotl now has text diffusion training. Read more here.
- 2025/08: QAT has been updated to include NVFP4 support. See PR.
- 2025/07:
- ND Parallelism support has been added into Axolotl. Compose Context Parallelism (CP), Tensor Parallelism (TP), and Fully Sharded Data Parallelism (FSDP) within a single node and across multiple nodes. Check out the blog post for more info.
- Axolotl adds more models: GPT-OSS, Gemma 3n, Liquid Foundation Model 2 (LFM2), and Arcee Foundation Models (AFM).
- FP8 finetuning with fp8 gather op is now possible in Axolotl via
torchao. Get started here! - Voxtral, Magistral 1.1, and Devstral with mistral-common tokenizer support has been integrated in Axolotl!
- TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!
- 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!
Expand older updates
- 2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.
- 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See docs to start training your own Magistral models with Axolotl!
- 2025/04: Llama 4 support has been added in Axolotl. See docs to start training your own Llama 4 models with Axolotl's linearized version!
- 2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!
- 2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.
- 2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!
- 2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.
✨ Overview
Axolotl is a free and open-source tool designed to streamline post-training and fine-tuning for the latest large language models (LLMs).
Features:
- Multiple Model Support: Train various models like GPT-OSS, LLaMA, Mistral, Mixtral, Pythia, and many more models available on the Hugging Face Hub.
- Multimodal Training: Fine-tune vision-language models (VLMs) including LLaMA-Vision, Qwen2-VL, Pixtral, LLaVA, SmolVLM2, and audio models like Voxtral with image, video, and audio support.
- Training Methods: Full fine-tuning, LoRA, QLoRA, GPTQ, QAT, Preference Tuning (DPO, IPO, KTO, ORPO), RL (GRPO), and Reward Modelling (RM) / Process Reward Modelling (PRM).
- Easy Configuration: Re-use a single YAML configuration file across the full fine-tuning pipeline: dataset preprocessing, training, evaluation, quantization, and inference.
- Performance Optimizations: Multipacking, Flash Attention, Xformers, Flex Attention, Liger Kernel, Cut Cross Entropy, Sequence Parallelism (SP), LoRA optimizations, Multi-GPU training (FSDP1, FSDP2, DeepSpeed), Multi-node training (Torchrun, Ray), and many more!
- Flexible Dataset Handling: Load from local, HuggingFace, and cloud (S3, Azure, GCP, OCI) datasets.
- Cloud Ready: We ship Docker images and also PyPI packages for use on cloud platforms and local hardware.
🚀 Quick Start - LLM Fine-tuning in Minutes
Requirements:
- NVIDIA GPU (Ampere or newer for
bf16and Flash Attention) or AMD GPU - Python 3.11
- PyTorch ≥2.8.0
Google Colab
Installation
Using pip
pip3 install -U packaging==26.0 setuptools==75.8.0 wheel ninja
pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
# Download example axolotl configs, deepspeed configs
axolotl fetch examples
axolotl fetch deepspeed_configs # OPTIONAL
Using Docker
Installing with Docker can be less error prone than installing in your own environment.
docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
Other installation approaches are described here.
Cloud Providers
Your First Fine-tune
# Fetch axolotl examples
axolotl fetch examples
# Or, specify a custom path
axolotl fetch examples --dest path/to/folder
# Train a model using LoRA
axolotl train examples/llama-3/lora-1b.yml
That's it! Check out our Getting Started Guide for a more detailed walkthrough.
📚 Documentation
- Installation Options - Detailed setup instructions for different environments
- Configuration Guide - Full configuration options and examples
- Dataset Loading - Loading datasets from various sources
- Dataset Guide - Supported formats and how to use them
- Multi-GPU Training
- Multi-Node Training
- Multipacking
- API Reference - Auto-generated code documentation
- FAQ - Frequently asked questions
🤝 Getting Help
- Join our Discord community for support
- Check out our Examples directory
- Read our Debugging Guide
- Need dedicated support? Please contact ✉️wing@axolotl.ai for options
🌟 Contributing
Contributions are welcome! Please see our Contributing Guide for details.
📈 Telemetry
Axolotl has opt-out telemetry that helps us understand how the project is being used and prioritize improvements. We collect basic system information, model types, and error rates—never personal data or file paths. Telemetry is enabled by default. To disable it, set AXOLOTL_DO_NOT_TRACK=1. For more details, see our telemetry documentation.
❤️ Sponsors
Interested in sponsoring? Contact us at wing@axolotl.ai
📝 Citing Axolotl
If you use Axolotl in your research or projects, please cite it as follows:
@software{axolotl,
title = {Axolotl: Open Source LLM Post-Training},
author = {{Axolotl maintainers and contributors}},
url = {https://github.com/axolotl-ai-cloud/axolotl},
license = {Apache-2.0},
year = {2023}
}
📜 License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.