# axolotl

Walks your agent through Axolotl training runs: YAML configs, LoRA and QLoRA, DPO, KTO, ORPO and GRPO methods, dataset formats.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 960 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/03-fine-tuning/axolotl/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/axolotl

## What it does

The skill drops an Axolotl reference into your agent's context, built from the official docs. It ships three files under `references/`: `api.md`, `dataset-formats.md` and `other.md`. The agent opens them with `view` when it needs a specific API detail or dataset layout.

The quick reference holds ready-made config fragments. FSDP setup via `fsdp_version: 2` plus an `fsdp_config` block with `offload_params` and `state_dict_type`, for instance. Or `save_compressed: true`, which shrinks the checkpoint and keeps it loadable by vLLM. There are notes on `context_parallel_size` too: it has to divide the GPU count evenly, otherwise the global batch size drops.

This skill does not replace Axolotl itself. Its dependencies list `axolotl`, `torch`, `transformers`, `datasets`, `peft`, `accelerate` and `deepspeed` — you install those in your own environment. The docs were generated automatically, so for version-specific behaviour check the upstream repo.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/axolotl/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/axolotl/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/axolotl/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/axolotl/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs axolotl
```

2. **Manually: put the files in place**
- File: `.github/skills/axolotl/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 03-fine-tuning/axolotl
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/axolotl/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/03-fine-tuning/axolotl/SKILL.md

## When to use it
- write a YAML config for fine-tuning a model
- pick between LoRA and QLoRA for the memory you have
- set up FSDP with parameter offloading across GPUs
- figure out the dataset format Axolotl expects
- switch from plain SFT to DPO or GRPO

## Example prompts
- Help me write an Axolotl YAML config for QLoRA fine-tuning
- How do I enable FSDP with parameter offloading in Axolotl?
- Check my Axolotl config: why does the global batch size drop with context_parallel_size=4?

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 445584311a65070f.

BEGIN THIRD-PARTY FILE 445584311a65070f
````text
---
name: axolotl
description: Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Fine-Tuning, Axolotl, LLM, LoRA, QLoRA, DPO, KTO, ORPO, GRPO, YAML, HuggingFace, DeepSpeed, Multimodal]
dependencies: [axolotl, torch, transformers, datasets, peft, accelerate, deepspeed]
---

# Axolotl Skill

Comprehensive assistance with axolotl development, generated from official documentation.

## When to Use This Skill

This skill should be triggered when:
- Working with axolotl
- Asking about axolotl features or APIs
- Implementing axolotl solutions
- Debugging axolotl code
- Learning axolotl best practices

## Quick Reference

### Common Patterns

**Pattern 1:** To validate that acceptable data transfer speeds exist for your training job, running NCCL Tests can help pinpoint bottlenecks, for example:

```
./build/all_reduce_perf -b 8 -e 128M -f 2 -g 3
```

**Pattern 2:** Configure your model to use FSDP in the Axolotl yaml. For example:

```
fsdp_version: 2
fsdp_config:
  offload_params: true
  state_dict_type: FULL_STATE_DICT
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: LlamaDecoderLayer
  reshard_after_forward: true
```

**Pattern 3:** The context_parallel_size should be a divisor of the total number of GPUs. For example:

```
context_parallel_size
```

**Pattern 4:** For example: - With 8 GPUs and no sequence parallelism: 8 different batches processed per step - With 8 GPUs and context_parallel_size=4: Only 2 different batches processed per step (each split across 4 GPUs) - If your per-GPU micro_batch_size is 2, the global batch size decreases from 16 to 4

```
context_parallel_size=4
```

**Pattern 5:** Setting save_compressed: true in your configuration enables saving models in a compressed format, which: - Reduces disk space usage by approximately 40% - Maintains compatibility with vLLM for accelerated inference - Maintains compatibility with llmcompressor for further optimization (example: quantization)

```
save_compressed: true
```

**Pattern 6:** Note It is not necessary to place your integration in the integrations folder. It can be in any location, so long as it’s installed in a package in your python env. See this repo for an example: https://github.com/axolotl-ai-cloud/diff-transformer

```
integrations
```

**Pattern 7:** Handle both single-example and batched data. - single example: sample[‘input_ids’] is a list[int] - batched data: sample[‘input_ids’] is a list[list[int]]

```
utils.trainer.drop_long_seq(sample, sequence_len=2048, min_sequence_len=2)
```

### Example Code Patterns

**Example 1** (python):
```python
````
END THIRD-PARTY FILE 445584311a65070f

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