---
type: concept
x-llmwiki:
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  pageDirectory: concepts
  sources:
    - fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - axolotl
    - axolotl-framework-for-llm-fine-tuning
    - AFFLF
  citations:
    - file: fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md
title: Axolotl
description: A high-performance framework for LLM fine-tuning that supports QLoRA and other efficient training techniques, with integration for MLflow, HuggingFace, and distributed training
tags:
  - machine-learning
  - fine-tuning
  - framework
timestamp: "2026-06-19T18:51:14.530Z"
---

```yaml
---
title: Axolotl
summary: High-performance framework for LLM post-training with QLoRA, supporting fine-tuning on multi-GPU infrastructure
sources:
  - fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md
kind: concept
createdAt: "2026-06-19T10:35:02.543Z"
updatedAt: "2026-06-19T10:35:02.543Z"
tags:
  - machine-learning
  - fine-tuning
  - framework
aliases:
  - axolotl
confidence: 1
provenanceState: extracted
inferredParagraphs: 0
---

# Axolotl

**Axolotl** is a high-performance, open-source framework for fine-tuning and post-training large language models (LLMs). It provides a unified, configurable interface for applying [QLoRA](/concepts/qlora-quantized-low-rank-adaptation.md) (Quantized Low-Rank Adaptation), enabling efficient adaptation of large models on multi-GPU infrastructure. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Overview

Axolotl streamlines the process of LLM post-training by offering a config-driven approach to model adaptation. Its design emphasizes transparency and control, allowing users to specify every aspect of the training configuration — from model architecture and dataset loading to optimizer settings and logging — through a declarative configuration format (often YAML or Python `DictDefault`). ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Key Features

- **QLoRA support**: Axolotl specializes in QLoRA, a technique that combines 4-bit quantization and Low-Rank Adaptation to dramatically reduce GPU memory requirements while preserving model quality. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Memory-efficient training**: The framework integrates with memory optimization techniques such as [gradient checkpointing](/concepts/activation-checkpointing.md), [Cut Cross Entropy](/concepts/cut-cross-entropy.md) (a memory-efficient loss implementation), and [SDPA](/concepts/scaled-dot-product-attention-sdpa.md) (Scaled Dot Product Attention) for broader GPU compatibility. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Dataset flexibility**: Supports multiple dataset formats, including Alpaca format and [chat template format](/concepts/chat-template-formatting-for-fine-tuning.md), with built-in preprocessing for sample packing and filtering of long samples. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Sample packing**: Multiple training samples are packed into a single sequence to maximize GPU utilization and trainable samples per step. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Distributed training**: Integrates natively with [multi-GPU distributed training](/concepts/multi-gpu-distributed-training-api.md) environments, including [Serverless GPU Compute](/concepts/serverless-gpu-compute.md) platforms like Databricks, where the `@distributed` decorator can orchestrate training across multiple GPUs (e.g., 8× H100). ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **MLflow integration**: Supports tracking and logging of training metrics to [MLflow](/concepts/mlflow.md), allowing experiment run metadata (such as run IDs) to be captured for model registration and deployment. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Cut Cross-Entropy plugin**: Includes the `axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin` for memory-efficient loss computation, reducing the memory footprint during forward/backward passes. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Architecture

Axolotl's training pipeline is organized around a modular architecture:

1. **Configuration loading** — `load_cfg()` reads and validates the user-provided configuration. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
2. **Dataset loading** — `load_datasets()` parses, tokenizes, and prepares the dataset, dropping samples that overflow the maximum sequence length. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
3. **Training** — The `train()` function runs the optimization loop; for demo purposes, `max_steps` can be set to a small number (e.g., 16) to verify the pipeline. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
4. **Post-training** — After training, LoRA adapters can be merged into the base model and the combined model logged and registered for deployment. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Ecosystem

### Supported Models

Axolotl supports a wide range of LLMs that are compatible with the HuggingFace ecosystem, including Olmo3 7B, Llama, Qwen, and other models with standard [transformers](/concepts/mlflow-transformers-flavor.md) interfaces. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

### Common Workflows

- **QLoRA on Serverless GPUs**: Particularly suited for limited-capacity cloud environments where A100/H100 GPUs may be scarce; QLoRA reduces memory requirements so that a 7B model can fit on 8× H100 GPUs. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Multi-step demo training**: Typical Axolotl workflows run a small number of steps (e.g., 16) to demonstrate the pipeline, using sample packing to maximize trainable samples per step. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]
- **Model merging and registration**: After training, LoRA adapters can be merged into the base model using `PeftModel.merge_and_unload()` and the combined model registered to a model registry (e.g., [Unity Catalog](/concepts/unity-catalog.md)) for deployment. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Telemetry

Axolotl's usage tracking can be disabled by setting the environment variable `AXOLOTL_DO_NOT_TRACK=1` before configuration. ^[fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md]

## Related Concepts

- Fine-tuning — The process of adapting a pre-trained model to a new task or dataset.
- [QLoRA](/concepts/qlora-quantized-low-rank-adaptation.md) — A parameter-efficient fine-tuning technique combining quantization and LoRA.
- [Quantization](/concepts/mxfp4-quantization.md) — Representing model weights with fewer bits to reduce memory.
- Sample Packing — Combining multiple training samples into a single sequence.
- [Cut Cross Entropy](/concepts/cut-cross-entropy.md) — A memory-efficient implementation of the cross-entropy loss.
- [HuggingFace Transformers](/concepts/hugging-face-transformers-trainer.md) — The library providing model architectures and tokenizers.
- [MLflow](/concepts/mlflow.md) — The experiment tracking and model registry platform.
- [Unity Catalog](/concepts/unity-catalog.md) — Databricks' catalog for managing model assets.
- [Serverless GPU Compute](/concepts/serverless-gpu-compute.md) — A cloud service that provides GPU resources without manual cluster management.
- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md) — Techniques for scaling model training across multiple GPUs.

## Sources

- fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md

# Citations

1. [fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws.md](/references/fine-tune-olmo3-7b-with-axolotl-on-multi-gpu-serverless-compute-databricks-on-aws-c7178be1.md)
