docs/dataset-formats/conversation.qmd

---
title: Conversation
description: Conversation format for supervised fine-tuning.
order: 1
---

## Formats

### sharegpt

conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)

```{.json filename="data.jsonl"}
{"conversations": [{"from": "...", "value": "..."}]}
```

Note: `type: sharegpt` opens a special config `conversation:` that enables conversions to many Conversation types. See [the docs](../docs/config.qmd) for all config options.

### pygmalion

```{.json filename="data.jsonl"}
{"conversations": [{"role": "...", "value": "..."}]}
```

### sharegpt.load_role

conversations where `role` is used instead of `from`

```{.json filename="data.jsonl"}
{"conversations": [{"role": "...", "value": "..."}]}
```

### sharegpt.load_guanaco

conversations where `from` is `prompter` `assistant` instead of default sharegpt

```{.json filename="data.jsonl"}
{"conversations": [{"from": "...", "value": "..."}]}
```

### sharegpt_jokes

creates a chat where bot is asked to tell a joke, then explain why the joke is funny

```{.json filename="data.jsonl"}
{"conversations": [{"title": "...", "text": "...", "explanation": "..."}]}
```

## How to add custom prompts for instruction-tuning

For a dataset that is preprocessed for instruction purposes:

```{.json filename="data.jsonl"}
{"input": "...", "output": "..."}
```

You can use this example in your YAML config:

```{.yaml filename="config.yaml"}
datasets:
  - path: repo
    type:
      system_prompt: ""
      field_system: system
      field_instruction: input
      field_output: output
      format: "[INST] {instruction} [/INST]"
      no_input_format: "[INST] {instruction} [/INST]"
```

See full config options under [here](../docs/config.qmd).