# Fine Tuning Kiln makes it easy to fine-tune a wide variety of models like GPT-4o, Llama, Mistral, Gemma, and many more. Check out our fine tuning docs to get started:
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Fine Tuning GuideOur end-to-end walkthrough of fine-tuning a model in Kiln. Includes generating training data./files/reaAySjXgTjdVkcX2Tzy/pages/UQ5mUIArq32hihhGE4Sv
Train a Reasoning ModelUsing a process called distillation, create your own reasoning model./files/Q49HXGUBF6ORGBiL01mv/pages/RaqHbAJUWPl2zk68NF3E
Fine Tune For Tool UseTrain a model to use a specific set of tools, at the right time, with the right parameters./files/RqmwElvFlPnm4q3vJ1Nq/pages/SbUpu30PLNTEjXCKTNOK
{% hint style="success" %} **Before Fine-Tuning, consider using Kiln's** [**Automatic Prompt Optimizer**](/docs/prompts/automatic-prompt-optimizer.md)**.** It has many of the benefits of fine-tuning (automated data driven optimization), while the result is much easier to deploy than a fine-tuned model. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.kiln.tech/docs/fine-tuning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.