Axolotl Fine-Tuning Tutorial 2026: Config-Driven LLM Training With Real Code
Training scripts spiral into 500 lines of boilerplate. Axolotl (12k stars) replaces them with one YAML config, and it powers production fine-tunes across the open source community.
💡 What You Will Learn
Training scripts spiral into 500 lines of boilerplate. Axolotl (12k stars) replaces them with one YAML config, and it powers production fine-tunes across the open source community.
📜 Table of Contents
The Config-First Philosophy
Axolotl (12,344 stars, fetched 2026-08-13) is the fine-tuning framework that decided every training decision belongs in a YAML file, not in Python glue. Want LoRA? Set it in config. Want QLoRA with 4-bit? Two lines. Want sequence packing, gradient checkpointing, or a custom chat template? All config. The result: reproducible training you can diff in code review.
A Minimal Config That Actually Trains
base_model: Qwen/Qwen2.5-7B
model_type: AutoModelForCausalLM
load_in_4bit: true
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
sequences:
- sequence_len: 2048
sample_packing: true
datasets:
- path: your_dataset.jsonl
type: chat_template
training:
num_epochs: 1
micro_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 2e-4
optimizer: adamw_torch
scheduler: cosine
warmup_steps: 20
This is a working QLoRA setup: a 7B model trains on one 24 GB GPU. The sample_packing line is the performance trick - it packs multiple short samples into one sequence, which can double training throughput versus naive batching.
Install and Run
git clone https://github.com/axolotl-ai-cloud/axolotl
cd axolotl && pip install -e .
accelerate launch -m axolotl.cli.train config.yml
Axolotl handles the Hugging Face Trainer underneath, so you get the usual checkpoints, logging and evaluation callbacks. When training finishes, merge the adapter back into the base model:
accelerate launch -m axolotl.cli.merge_lora config.yml
When Axolotl Beats Unsloth and TRL
- Team workflows: configs live in Git, so a full fine-tune setup is reviewable and rollback-able.
- Exotic setups: FSDP, DeepSpeed ZeRO, flash attention, multi-node - Axolotl exposes them as config flags instead of rewrite work.
- Experiments: sweep LoRA ranks and learning rates by editing YAML, not code.
Unsloth (70,566 stars) is faster per step and simpler for one-off jobs; TRL is the bare-metal library. Axolotl is the middle ground that production teams actually commit to Git.
Start Small
Take any instruction dataset with 1,000 samples, run the config above for one epoch on a single GPU, and evaluate against the base model with the same 20 prompts. You will see exactly where fine-tuning helps - and where your data, not the method, is the bottleneck.
