axolotl/docs/dataset-formats/conversation.qmd

---
title: Conversation
description: Conversation format for supervised fine-tuning.
order: 3
---

## chat_template

Chat Template strategy uses a jinja2 template that converts a list of messages into a prompt. Support using tokenizer's template, a supported template, or custom jinja2.

```{.json filename="data.jsonl"}
{"messages": [{"role": "...", "content": "..."}, {"role": "...", "content": "..."}, ...]}
```

See [configs](../config-reference.qmd) for full configs and supported templates.

### Migrating from sharegpt

Most configs can be adapted as follows:

```yaml
# old
chat_template: chatml
datasets:
  - path: ...
    type: sharegpt
    conversation: chatml

# new (if using tokenizer's chat_template)
datasets:
  - path: ...
    type: chat_template

    field_messages: conversations
    message_property_mappings:
      role: from
      content: value

# new (if setting a new chat_template like chatml, gemma, etc)
chat_template: chatml
datasets:
  - path: ...
    type: chat_template

    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
```

We recommend checking the below examples for other usecases.

### Examples

#### Training on last message

(Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.

```yaml
datasets:
  - path: ...
    type: chat_template
    roles_to_train:
    train_on_eos:
```

::: {.callout-tip}
If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
:::

#### Overriding default chat template

Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.

```yaml
chat_template: gemma # this overwrites the tokenizer's chat_template
datasets:
  - path: ...
    type: chat_template
    roles_to_train: ["assistant"]  # default value
```

::: {.callout-note}
If you want to use built-in chat_template, use `chat_template: tokenizer_default` (this is set by default).
:::

#### Using default chat template with fallback

Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.

```yaml
chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
datasets:
  - path: ...
    type: chat_template
```

#### Custom Jinja template

Using a custom jinja template on OpenAI messages format, training on all assistant messages.

```yaml
# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
chat_template_jinja: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|system|>' + '\n' + message['content'] + '<|end|>' + '\n'}}{% elif (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif message['role'] == 'assistant' %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}"

datasets:
  - path: ...
    type: chat_template
```

::: {.callout-important}
Please make sure that your `tokenizer.eos_token` is same as EOS (End-of-Sequence) token in template. Otherwise, set `eos_token` under `special_tokens: `.
:::

#### Using template with different token for EOT and EOS

- If you are using a template that has a different EOT (End-of-Turn) token from EOS token or multiple EOT tokens (like Mistral V7 Tekken), set the `eot_tokens: ` config. The handling of EOT tokens follows `train_on_eos: ` which defaults to turn.

```yaml
eot_tokens:
  - "[/INST]"
  # - "[/SYSTEM_PROMPT]"

datasets:
  - path: ...
    type: chat_template

    # optional
    train_on_eot: turn  # defaults read from train_on_eos (which defaults to turn)
```

::: {.callout-tip}
See [config documentation](../config-reference.qmd) for detailed explanations of "turn", "last", and "all" options for training on tokens.
:::

::: {.callout-note}
Using `eot_tokens` requires each token that exists in `chat_template` to be a single token in the tokenizer. Otherwise, the tokenizer will split the token and cause unexpected behavior.

You can add those tokens as new tokens under `tokens: ` or (recommended) override unused added_tokens via `added_tokens_overrides: `. See [config](../config-reference.qmd) for more details.
:::

- Continuing from the previous example, if you want to train on all EOT token trainable turns but only last EOS token, set `train_on_eos: last`.

```yaml
eot_tokens:
  - "[/INST]"
  # ...

datasets:
  - path: ...
    type: chat_template

    train_on_eos: last
    train_on_eot: turn
```

::: {.callout-tip}
If EOS token only appears at the end of a prompt, `train_on_eos: last` is equivalent to `train_on_eos: turn`. Therefore, generally, you can leave them to their defaults and omit them.
:::


#### Using tool use

Instead of passing `tools` via the system prompt, an alternative method would be to have the `tools` in a separate column and loaded via `chat_template` to let the template dynamically build it.

```json
{
    "tools": [
        {
            "type": "...",
            "function": {
                "name": "...",
                "description": "...",
                "parameters": {
                    "type": "...",
                    "properties": {
                        // ...
                    },
                    "required": ["..."],
                },
            },
        },
    ],
    "messages": [
        // ...
        {
            "role": "assistant", // call the function via assistant
            "tool_calls": [
                {
                    "id": "...",  // required only for mistral
                    "type": "function",
                    "function": {
                        "name": "...",
                        "arguments": {
                            "...": "...",
                        }
                    }
                }
            ]
        },
        {
            "role": "tool",
            "tool_call_id": "...",  // required only for mistral
            "name": "...",
            "content": "..."
        },
    ],
}
```

::: {.callout-note}
Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
:::

::: {.callout-warning}
If you have tool arguments with same name but different dtypes (like `"time": string` and `"time": number`), please save `arguments: ` as JSON string to prevent `datasets` from having casting issues.

```
"arguments": "{\"...\": \"...\"}"
```

The same is applicable for tool parameters.

```
"parameters": "{\"...\": \"...\"}"
```

:::

Example config for Llama4:
```yaml
chat_template: llama4
datasets:
  - path: Nanobit/text-tools-2k-test
    type: chat_template
    # field_tools: tools # default is `tools`
```

::: {.callout-tip}
Look into the `chat_template` you are using to see if it supports `tools` and what the expected role is for the tool answer. In the example above, the tool answer is expected to be in the `tool` or `ipython` role for `llama4` template.
:::


#### Using fine-grained control over token masking

(Advanced) Using fine-grained control over tokens and turns to train in a conversation

For a data sample that looks like:

```{.json filename="data.jsonl"}
{
  "conversations": [
    {"from": "system", "value": "You are an AI assistant.", "train": false},
    {"from": "human", "value": "Hello", "train": false},
    {"from": "assistant", "value": "Hello", "train": true},
    {"from": "human", "value": "How are you?", "train": true},
    {
      "from": "assistant",
      "value": "I'm doing very well, thank you!",
      "train_detail": [
        {"begin_offset": 0, "end_offset": 8, "train": false},
        {"begin_offset": 9, "end_offset": 18, "train": true},
        {"begin_offset": 19, "end_offset": 30, "train": false},
      ],
    },
    {
        "from": "human",
        "value": "I'm doing very well, thank you!",
        "train": true,
    },
    {"from": "assistant", "value": "Hi there!", "train": true}
  ]
}
```

The configuration would look like:

```yaml
datasets:
  - path: ...
    type: chat_template
    chat_template: tokenizer_default
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles_to_train: []
    train_on_eos: turn
    message_field_training: train
    message_field_training_detail: train_detail
```

::: {.callout-tip}
It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
:::

#### Reasoning split

(For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.

```yaml
datasets:
  - path: ...
    type: chat_template
    chat_template: qwen3
    split_thinking: true
```

For example, a content can look like:

```json
{
  "content": "<think>Some thinking outputs</think>Output after thinking."
}
```

After split, it will look like:

```json
{
  "reasoning_content": "Some thinking outputs",
  "content": "Output after thinking..."
}
```


## sharegpt

::: {.callout-important}
ShareGPT is deprecated!. Please see [chat_template](#chat_template) section.
:::

## pygmalion

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