Guide · Agent skill
How to use the Axolotl LLM Fine-Tuning Guide agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The axolotl skill is a set of reference notes and short code snippets from the Axolotl documentation that an AI agent can read when you ask about fine-tuning large language models. Axolotl is an open-source tool that is configured with YAML files rather than custom code. After following this guide you will know how to install the skill, ask for it in plain language, and use the agent's answers to draft or debug a fine-tuning config.
Axolotl LLM Fine-Tuning GuideOrchestra-Research/AI-Research-SKILLs · MIT Download the skill Quick start
- 1Download the ZIP. Get the axolotl skill ZIP from this page.
- 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for personal use or into .claude/skills/ in a project.
- 3Or upload it in the Claude apps. Go to Settings → Capabilities → Skills and upload the skill.
- 4Ask a specific question. Describe your model, dataset format, GPU setup and goal in plain language.
- 5Review the draft config. Compare the agent's YAML against the official Axolotl docs before using it.
- 6Test on a small run. Run a short training job on your own hardware to confirm the config behaves as expected.
What the axolotl skill makes the agent do
Once installed, the agent knows the Axolotl open-source tool for training and fine-tuning large language models. The skill gives it patterns for common choices: parameter-efficient methods such as LoRA and QLoRA, preference-tuning methods such as DPO, KTO, ORPO and GRPO, distributed training with FSDP or DeepSpeed, and dataset formats.
The skill is mostly reference material pulled from the official documentation, so the agent can answer questions and draft configs more accurately. It does not run any training itself.
Install the axolotl skill
Download the ZIP from this page and unzip it. Put the resulting axolotl folder in ~/.claude/skills/ to make it available to you across projects, or in a project's .claude/skills/ folder to limit it to that project.
You can also upload the skill in the Claude apps under Settings → Capabilities → Skills.
- Personal install: ~/.claude/skills/
- Project install: .claude/skills/ inside your project
- Claude apps: Settings → Capabilities → Skills, then upload
Trigger the skill with natural requests
You do not need a special command. Ask about Axolotl in ordinary language and mention the task, the model or the dataset format. The skill's description covers YAML configs, LoRA/QLoRA, DPO/KTO/ORPO/GRPO and multimodal support, so requests on those topics are the most likely to use it.
- Draft a YAML config for LoRA fine-tuning of a 7B model on a chat dataset in ShareGPT-style JSONL.
- Which Axolotl settings do I need for DPO versus KTO?
- Show me an FSDP config for a Llama-based model on 8 GPUs.
- Why does my context_parallel_size setting fail, and what should it divide into?
Examples of what to ask for
The reference files include a FSDP example in Axolotl YAML with fsdp_version 2 and options such as offload_params, state_dict_type FULL_STATE_DICT and TRANSFORMER_BASED_WRAP. Use it as a starting point and check each option against the official docs.
The dataset-formats reference shows pre-tokenized data as JSONL rows with input_ids, attention_mask and labels, and a datasets block that sets path, ds_type and type. Ask the agent to explain which format matches your file.
datasets:
- path: /path/to/your/file.jsonl
ds_type: json
type:What you need before you start
The skill itself only needs to be read. To actually run training you need Axolotl and its Python dependencies (torch, transformers, datasets, peft, accelerate and deepspeed), GPU hardware for practical fine-tuning, and dataset preparation. Optional extras are NCCL tests for bandwidth checks and a Modal cloud account if you use the Modal integration referenced in the docs.
Safety notes: scripts and network
The skill runs no scripts and uses no network access. It is a set of Markdown reference files, so reading it does not change your system. Any training job you launch from an Axolotl config is a separate step that uses your own hardware and, if you choose, a cloud account.
Tips for better answers
The skill was generated automatically from documentation and some sections are incomplete. For example, the context_parallel_size pattern is only a bare name with no example. When an answer looks thin, ask the agent to point to the relevant reference file and then check the official Axolotl docs.
Give the agent your model name, GPU count, dataset format and the exact error message. Specific context produces more useful config drafts than general questions.
- Always test a generated config on a small run before a full job.
- Check any option against the official Axolotl docs and your own results.
What people use it for
- Drafting a LoRA or QLoRA YAML config for a specific model and dataset.
- Choosing between preference-tuning methods such as DPO, KTO, ORPO and GRPO.
- Planning multi-GPU training with FSDP, DeepSpeed or context parallelism.
- Checking a dataset file against the expected Axolotl format before training.
- Debugging a failing Axolotl config by describing the error to the agent.
Frequently asked questions
Is the axolotl skill free?
The skill is licensed under MIT, as listed on this page. Axolotl itself is an open-source tool, and you still need your own hardware or cloud account to run training.
What do I need to install to use the axolotl skill?
You only need to unzip the skill into a skills folder or upload it in the Claude apps. Axolotl and its Python dependencies are needed only if you want to run training.
Does the axolotl skill run training for me?
No. The skill provides reference notes and code snippets that help the agent answer questions and draft configs. Training runs only when you run Axolotl yourself.
Does the axolotl skill need internet access or run scripts?
According to this page, the skill runs no scripts and uses no network access. It consists of Markdown reference files.
How is this different from the official Axolotl docs?
The skill is a scraped, auto-generated summary of the official documentation, and some sections are incomplete. Use it for quick guidance, then confirm details against the official docs.
Which fine-tuning methods does the skill cover?
It covers LoRA and QLoRA, preference-tuning methods DPO, KTO, ORPO and GRPO, distributed training with FSDP or DeepSpeed, dataset formats, and multimodal support.
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Our verdict
Use this skill if you are an ML engineer or developer who wants an agent to explain Axolotl options or draft a YAML config for a specific model and dataset. Pick something else if you are not comfortable with GPUs and ML tooling, or if you need verified, up-to-date answers for production decisions, in which case go straight to the official Axolotl docs.
Download the skill