Axolotl — Streamlined LLM Fine-Tuning

What it is

Axolotl is a framework that streamlines post-training and fine-tuning for large language models. It supports LoRA, QLoRA, full fine-tuning, DPO, GRPO, and multimodal training. You define your entire training run in a single YAML configuration file, and Axolotl handles data loading, tokenization, training, and evaluation.

Axolotl targets ML engineers who want to fine-tune foundation models without writing custom training scripts for each experiment.

How it saves time or tokens

Axolotl eliminates boilerplate training code. Instead of writing data loaders, configuring optimizers, and managing device placement, you specify parameters in YAML and run one command. Switching from LoRA to QLoRA to full fine-tuning is a config change, not a code rewrite.

Flash Attention integration and multi-GPU support via DeepSpeed mean training runs faster without manual optimization.

How to use

1. Install Axolotl: pip install axolotl
2. Copy an example YAML config for your model and training method
3. Customize the config with your dataset, model, and hyperparameters
4. Run training: accelerate launch -m axolotl.cli.train config.yml

Example

# config.yml
base_model: meta-llama/Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_4bit: true
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05

datasets:
  - path: tatsu-lab/alpaca
    type: alpaca

sequence_len: 4096
micro_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 0.0002
num_epochs: 3
optimizer: adamw_bnb_8bit

flash_attention: true
wandb_project: my-finetune

Run with: accelerate launch -m axolotl.cli.train config.yml

Related on TokRepo

• Coding tools -- ML development tools
• Research tools -- AI research frameworks

Common pitfalls

• QLoRA with 4-bit quantization requires bitsandbytes, which only works on NVIDIA GPUs; Apple Silicon and AMD users need different quantization methods
• Incorrect sequence_len causes OOM or truncated training data; check your dataset's token length distribution first
• Flash Attention requires a compatible GPU (Ampere or newer); disable it on older hardware to avoid crashes