Agent skill · research science · nousresearch
axolotl
Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
Why this skill is useful
Provides specific YAML configurations and code patterns for fine-tuning LLMs with Axolotl that are not widely known.
What it needs
Requires accelerate, axolotl, datasets, deepspeed, peft, torch, transformers installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
Axolotl Skill 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. 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: Pattern 2: Configure your model to use FSDP in the Axolotl yaml. For example: Pattern 3: The contextparallelsize 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 contextparallelsize=4: Only 2 different batches processed per step (each split across 4 GPUs) - If your per-GPU microbatchsize is 2, the global batch size decreases from 16 to 4 Pattern 5: Setting savecompressed: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/axolotl