# Axolotl

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill axolotl -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/03-fine-tuning/axolotl/SKILL.md