# How to Leverage Axolotl for LLM Training and Fine-Tuning: A Complete Guide

> Learn to leverage Axolotl for LLM training and fine-tuning. This guide covers data preprocessing, quantization, and distributed scaling with a single config file.

- Repository: [Maxime Labonne/llm-course](https://github.com/mlabonne/llm-course)
- Tags: how-to-guide
- Published: 2026-03-01

---

**Axolotl is a declarative, YAML-driven framework that automates data preprocessing, quantization, LoRA adapter training, and distributed scaling, allowing you to fine-tune large language models with a single configuration file and one command.**

The `mlabonne/llm-course` repository identifies Axolotl as one of three primary fine-tuning toolkits alongside TRL and Unsloth, positioning it as the go-to solution for researchers who need reproducible, scalable training pipelines. By consolidating data ingestion, model loading, and distributed training orchestration into a unified interface, Axolotl eliminates the need for custom PyTorch scripts while maintaining full control over hyperparameters. This guide demonstrates how to leverage Axolotl for LLM training and fine-tuning using the exact workflow referenced at line 223 of the repository's [`README.md`](https://github.com/mlabonne/llm-course/blob/main/README.md).

## Understanding Axolotl's Architecture

Axolotl automates six critical components of the fine-tuning pipeline through a single YAML configuration file. According to the `mlabonne/llm-course` source, the framework handles everything from chat template formatting to multi-GPU scaling:

| Component | Implementation Details |
|-----------|------------------------|
| **Data ingestion** | Loads JSON, JSONL, CSV, or HuggingFace datasets, then applies configurable chat templates (ChatML, Alpaca, etc.) before tokenization. |
| **Model loading** | Uses `AutoModelForCausalLM` from Transformers with automatic 8-bit/4-bit quantization detection for memory-efficient training. |
| **PEFT integration** | Integrates directly with the PEFT library; specify [`lora.r`](https://github.com/mlabonne/llm-course/blob/main/lora.r), `lora.alpha`, and target modules in YAML. |
| **Distributed training** | Leverages DeepSpeed ZeRO-3 or FSDP via the `strategy` config key—set `deepspeed` or `fsdp` without code changes. |
| **Optimization** | Abstracts learning-rate schedulers, gradient accumulation, mixed-precision, and checkpointing through the `trainer` configuration block. |
| **Logging** | Built-in callbacks stream loss, perplexity, and custom metrics to WandB or MLflow after each epoch. |

This declarative approach means you can move from a single-GPU prototype to a multi-node cluster by changing only the `strategy` field in your configuration.

## Installation and Environment Setup

Before running your first training job, install Axolotl with the optional DeepSpeed dependencies for distributed training support.

```bash
pip install axolotl[deepspeed] peft transformers accelerate bitsandbytes

```

Configure Accelerate to auto-detect your hardware topology:

```bash
accelerate config

```

The wizard automatically selects the appropriate DeepSpeed ZeRO stage based on your GPU count and memory constraints.

## Creating Your Training Configuration

Axolotl uses a single YAML file to define the entire training pipeline. The `mlabonne/llm-course` repository demonstrates this pattern in its LazyAxolotl notebook, referenced at line 36 of [`README.md`](https://github.com/mlabonne/llm-course/blob/main/README.md).

### Example Configuration File

Create [`axolotl_config.yaml`](https://github.com/mlabonne/llm-course/blob/main/axolotl_config.yaml) with the following structure:

```yaml
model_name_or_path: meta-llama/CodeLlama-7b-hf
tokenizer_name: meta-llama/CodeLlama-7b-hf
datasets:
  - path: HuggingFaceH4/ultrafeedback_binarized
    split: train
    chat_template: alpaca
output_dir: ./output/code_llama_axolotl

# Quantization & PEFT

quantization: bitsandbytes
lora:
  r: 64
  alpha: 16
  target_modules: ["q_proj", "v_proj"]

# Training hyperparameters

trainer:
  per_device_train_batch_size: 4
  gradient_accumulation_steps: 8
  learning_rate: 2e-4
  num_train_epochs: 3
  warmup_steps: 100
  lr_scheduler_type: cosine
  logging_steps: 10
  eval_steps: 200
  save_steps: 500

strategy: deepspeed

```

Each key in this configuration maps directly to Axolotl's internal trainer arguments, eliminating the need for separate data preprocessing scripts or manual model initialization.

## Launching Distributed Training

Execute your training run using Accelerate, which handles process spawning across GPUs:

```bash
accelerate launch -m axolotl.train -c axolotl_config.yaml

```

To switch from single-GPU training to multi-node FSDP, simply change `strategy: fsdp` in your YAML file and relaunch. Axolotl automatically partitions model states and gradients according to your chosen strategy, whether using DeepSpeed ZeRO-3 or PyTorch FSDP.

## Monitoring with Weights & Biases

Add real-time experiment tracking by extending the `trainer` block:

```yaml
trainer:
  report_to: wandb
  wandb_project: axolotl-finetune
  wandb_entity: your_username

```

This configuration streams loss curves, learning-rate schedules, and GPU utilization metrics to your WandB dashboard without modifying training scripts.

## Deploying Fine-Tuned Models

After training completes, merge your LoRA adapters into the base model for inference or export them separately using the PEFT library.

### Merging Adapters for Inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/CodeLlama-7b-hf", 
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/CodeLlama-7b-hf")

# Load Axolotl-trained LoRA weights

model = PeftModel.from_pretrained(base_model, "./output/code_llama_axolotl")
model.eval()

# Generate text

inputs = tokenizer(
    "Write a Python function that computes the nth Fibonacci number.", 
    return_tensors="pt"
).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```

The resulting merged model behaves exactly like a standard HuggingFace model, compatible with vLLM, Text Generation Inference, or direct deployment.

## Summary

- **Axolotl** provides a YAML-driven interface that consolidates data preprocessing, quantization, LoRA configuration, and distributed training into a single command.
- The `mlabonne/llm-course` repository references Axolotl at line 223 of [`README.md`](https://github.com/mlabonne/llm-course/blob/main/README.md) as a primary toolkit, offering the LazyAxolotl notebook for one-click Colab deployment.
- **Memory efficiency** comes from built-in 4-bit quantization and QLoRA support, enabling fine-tuning on single consumer GPUs.
- **Scalability** is achieved through DeepSpeed ZeRO-3 and FSDP integration, configurable via the `strategy` key without code modifications.
- **Reproducibility** is guaranteed by version-controlling all hyperparameters, dataset paths, and training arguments in a single configuration file.

## Frequently Asked Questions

### What hardware requirements are needed to leverage Axolotl for LLM training?

You can fine-tune 7B parameter models on a single GPU with 24GB VRAM using 4-bit quantization and LoRA adapters. For larger models or full fine-tuning, Axolotl's DeepSpeed and FSDP integrations support multi-GPU and multi-node configurations with minimal configuration changes.

### How does Axolotl differ from the TRL library?

While both libraries support LoRA fine-tuning, Axolotl emphasizes a **declarative YAML-based workflow** that abstracts the entire pipeline, whereas TRL requires more manual Python scripting for data collation and training loops. The `mlabonne/llm-course` repository lists both as complementary tools depending on your preference for configuration versus code.

### Can I use custom datasets with Axolotl?

Yes. Axolotl accepts JSON, JSONL, CSV, and HuggingFace datasets. You specify the path, split, and chat template (Alpaca, ChatML, etc.) in the `datasets` section of your YAML configuration, and the framework automatically handles tokenization and batching.

### Is it possible to resume training from a checkpoint?

Yes. Set `save_steps` in the `trainer` configuration to determine checkpoint frequency, then use the `resume_from_checkpoint` parameter in your YAML file or pass it as a command-line argument to `axolotl.train` to resume from the last saved state.