Fine-Tune Any LLM Without Writing Training Code: Axolotl's Single-YAML Approach

Axolotl turns fine-tuning into a single YAML config — pick your model from HuggingFace Hub, your dataset, your training method (SFT, LoRA, QLoRA, DPO, GRPO), and the framework handles quantization, tokenization, training, and evaluation without you writing a training loop.

What you'll be able to do after this:

• Write a single YAML config that controls everything: base model, LoRA rank/alpha, dataset path and format, batch size, and training method
• Run SFT, LoRA, and QLoRA jobs with two shell commands — no Python boilerplate
• Switch to DPO or GRPO alignment training by adding three lines to the same config

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The previous teachable posts covered LoRA theory, QLoRA on Colab with Unsloth, DPO, and GRPO. Axolotl is the framework that wraps all of it into a reproducible, version-controlled pipeline used by serious fine-tuning teams.

Why Axolotl instead of raw PEFT/TRL?

Wiring fine-tuning from scratch means assembling: model loader, quantization (BitsAndBytes/GGUF), PEFT adapter config, tokenizer setup, dataset formatting, training loop, evaluation, and checkpointing. Axolotl handles every layer. You write YAML; the framework runs the pipeline. The config is version-controlled, shareable, and repeatable — change one value (e.g., LoRA rank from 32 to 64), commit, and compare runs.

Minimal SFT + LoRA config

base_model: NousResearch/Llama-3.2-1B
load_in_4bit: true   # QLoRA when paired with adapter: lora
adapter: lora

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj

datasets:
  - path: mhenrichsen/alpaca_data_cleaned
    type: alpaca

output_dir: ./lora-output
sequence_len: 2048
micro_batch_size: 4
num_epochs: 3
learning_rate: 2.0e-4
warmup_steps: 100

Save this as config.yml. Any HuggingFace Hub model ID works as base_model.

Two commands to train

pip install axolotl

# Tokenize and cache the dataset (run once; reuse across experiments)
axolotl preprocess config.yml

# Train — Axolotl auto-detects GPU count
axolotl train config.yml

After training, run inference from the same config:

axolotl inference config.yml --lora-model-dir=./lora-output
# --gradio flag opens a browser chat UI

Or merge the LoRA adapter into the base model for a standalone checkpoint:

axolotl merge-lora config.yml --lora-model-dir=./lora-output

Switching to DPO alignment in three lines

Add these to your existing config — the rest stays the same:

rl: dpo
dpo_beta: 0.1
datasets:
  - path: your-preference-dataset
    type: chat_template.default
    chat_template: llama3

Axolotl dispatches to TRL's DPO trainer internally. You don't rewrite the training loop — you change the config.

Scaling to multi-GPU

For 70B+ models or faster iteration, add two lines:

deepspeed: zero2    # ZeRO stage 2 sharding across GPUs
num_processes: 4    # number of GPUs

Run with accelerate launch — Axolotl handles the rest via its DeepSpeed/DDP integration.

Unsloth vs Axolotl: Unsloth (covered in an earlier post) is fastest on consumer single-GPU hardware and excellent for Colab experimentation. Axolotl is the right choice when you want a reproducible, config-file-driven pipeline you can commit, share, and extend to multi-GPU or multiple training methods from the same base config.

Sources: Axolotl Fine-Tuning Tutorial: Train LLMs with a Single YAML Config — YouTube · Axolotl Quickstart — Official Docs · Axolotl GitHub