> ## Documentation Index
> Fetch the complete documentation index at: /docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Choose a training method

> Compare supervised fine-tuning and prompt gisting by input, output, model support, and intended use.

Training supports two methods: supervised fine-tuning (SFT) and prompt gisting. Both learn from conversations, but they solve different problems and produce different artifacts.

## Compare the methods

| Question | Supervised fine-tuning | Prompt gisting |
| - | - | - |
| What changes? | The model's behavior is adapted from labeled conversations. | A static prompt is replaced by learned soft tokens. |
| What input is required? | A frozen dataset slice of conversations with a split column. | The same dataset slice and a non-empty prompt text file. |
| What does it produce? | A LoRA adapter that runs with its Base model. | A full model containing the learned gist tokens. |
| Which catalog Base models are supported? | `qwen3-4b`, `qwen3-8b`, `qwen3-32b`, and `qwen3-235b`. | `qwen3-4b` and `qwen3-8b`. The larger full models exceed Model Version size limits. |
| Can it continue from an existing Model Version? | Yes, from a LoRA adapter Model Version in the same project. | Yes, from a LoRA adapter Model Version in the same project. |
| Main method-specific setting | LoRA rank: `8`, `16`, or `32`. | Gist tokens: `8`, `32`, `64`, or a supported fraction of the prompt. |

Both methods publish a downloadable Model Version. The platform does not host or serve that Model Version.

## Choose SFT

Choose supervised fine-tuning when examples should teach a model how to respond or classify. Each row ends with the assistant output the model should learn, such as a support-ticket label or a complete answer.

SFT is also the method to use when:

* You need a LoRA adapter rather than a full model.
* You need one of the larger `qwen3-32b` or `qwen3-235b` Base models.
* You want to continue adapting an earlier LoRA adapter.

SFT is the CLI default. You do not pass `--method` when creating an SFT Training.

## Choose gisting

Choose gisting when the reusable part of the task is a long, static instruction prompt. Gisting learns soft tokens from that prompt and the example conversations. The dynamic user input stays in each conversation.

Gisting is also the method to use when:

* Prompt compression is the goal, rather than adapting model weights for a new response behavior.
* You can use `qwen3-4b` or `qwen3-8b`.
* You need a full Model Version rather than a LoRA adapter.

A gisting Training always needs `--method gisting` and `--prompt-file`.

## Use the right product surface

Use the [CLI](/docs/cli.md) or [HTTP API](/docs/api-reference.md) for project Training. The web app's **Fine-tuning** and **Prompt gisting** pages help you set up a coding agent. **Fine-tune lab**, where available, has separate runs and downloads; see [Ways to work with Training](/docs/train/concepts/how-training-works.md#ways-to-work-with-training).

## Next steps

* [Train your first model](/docs/train/get-started/first-trained-model.md) for a guided SFT Training Run
* [Fine-tune a model](/docs/train/guides/fine-tune.md) for the complete SFT CLI workflow
* [Compress a prompt with gisting](/docs/train/guides/gist.md) for the complete gisting CLI workflow
* [Training settings](/docs/train/reference/settings.md) for every configurable value and default
* [Base models](/docs/train/reference/base-models.md) for the model catalog and pinned revisions


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