Download 03-fine-tuning/axolotl/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does axolotl live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use axolotl?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
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:
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]]
Use view to read specific reference files when detailed information is needed.
Working with This Skill
For Beginners
Start with the getting_started or tutorials reference files for foundational concepts.
For Specific Features
Use the appropriate category reference file (api, guides, etc.) for detailed information.
For Code Examples
The quick reference section above contains common patterns extracted from the official docs.
Resources
references/
Organized documentation extracted from official sources. These files contain:
Detailed explanations
Code examples with language annotations
Links to original documentation
Table of contents for quick navigation
scripts/
Add helper scripts here for common automation tasks.
assets/
Add templates, boilerplate, or example projects here.
Notes
This skill was automatically generated from official documentation
Reference files preserve the structure and examples from source docs
Code examples include language detection for better syntax highlighting
Quick reference patterns are extracted from common usage examples in the docs
Updating
To refresh this skill with updated documentation:
Re-run the scraper with the same configuration
The skill will be rebuilt with the latest information
How do I install axolotl?
Download 03-fine-tuning/axolotl/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does axolotl live?
The publisher keeps it at https://github.com/Orchestra-Research/AI-Research-SKILLs.
Which agents can use axolotl?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.