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Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).

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Skill metadata

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Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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Axolotl Skill

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What’s inside

Expert guidance for fine-tuning LLMs with Axolotl — YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support. Assistance with axolotl development, generated from official documentation.

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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

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Quick Reference

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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:
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:
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
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)
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
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]]

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Example Code Patterns

Example 1 (python):
Example 2 (python):
Example 3 (python):
Example 4 (python):
Example 5 (python):

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Reference Files

This skill includes comprehensive documentation in references/:
  • api.md - Api documentation
  • dataset-formats.md - Dataset-Formats documentation
  • other.md - Other documentation
Use view to read specific reference files when detailed information is needed.

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Working with This Skill

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For Beginners

Start with the getting_started or tutorials reference files for foundational concepts.

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For Specific Features

Use the appropriate category reference file (api, guides, etc.) for detailed information.

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For Code Examples

The quick reference section above contains common patterns extracted from the official docs.

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Resources

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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

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scripts/

Add helper scripts here for common automation tasks.

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assets/

Add templates, boilerplate, or example projects here.

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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

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Updating

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  2. The skill will be rebuilt with the latest information