github.com/axolotl-ai-cloud/axolotl @v0.17.0 sqlite

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  <strong>A Free and Open Source LLM Fine-tuning Framework</strong>









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🎉 Latest Updates

• 2026/04:
• New model support has been added in Axolotl for Mistral Medium 3.5 and Gemma 4.
• Axolotl is now uv-first and has SonicMoE fused LoRA support.
• 2026/03:
• New model support has been added in Axolotl for Mistral Small 4, Qwen3.5, Qwen3.5 MoE, GLM-4.7-Flash, GLM-4.6V, and GLM-4.5-Air.
• MoE expert quantization support (via quantize_moe_experts: true) greatly reduces VRAM when training MoE models (FSDP2 compat).
• 2026/02:
• ScatterMoE LoRA support. LoRA fine-tuning directly on MoE expert weights using custom Triton kernels.
• Axolotl now has support for SageAttention and GDPO (Generalized DPO).
• 2026/01:
• New integration for EAFT (Entropy-Aware Focal Training), weights loss by entropy of the top-k logit distribution, and Scalable Softmax, improves long context in attention.
• 2025/12:
• Axolotl now includes support for Kimi-Linear, Plano-Orchestrator, MiMo, InternVL 3.5, Olmo3, Trinity, and Ministral3.
• Distributed Muon Optimizer support has been added for FSDP2 pretraining.
• 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.

Expand older updates

• 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!
• 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See docs to start training your own Magistral models with Axolotl!
• 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!
• 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: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.
• 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, GLM-4.6V, InternVL 3.5, Gemma 3n, 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, GDPO), 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 2/3/4, Xformers, Flex Attention, SageAttention, Liger Kernel, Cut Cross Entropy, ScatterMoE, 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 bf16 and Flash Attention) or AMD GPU
• Python >=3.11 (3.12 recommended)
• PyTorch ≥2.9.1

Google Colab

Installation

# install uv if you don't already have it installed (restart shell after)
curl -LsSf https://astral.sh/uv/install.sh | sh

# change depending on system
export UV_TORCH_BACKEND=cu128

# create a new virtual environment
uv venv --python 3.12
source .venv/bin/activate

uv pip install torch==2.10.0 torchvision
uv pip install --no-build-isolation axolotl[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"' --ipc=host --rm -it axolotlai/axolotl:main-latest

Other installation approaches are described here.

Cloud Providers

• RunPod
• Vast.ai
• PRIME Intellect
• Modal
• Novita
• JarvisLabs.ai
• Latitude.sh

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