---
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-tip}
`chat_template_jinja` also accepts a file path to a `.jinja2` file instead of an inline string:

```yaml
chat_template_jinja: ./path/to/my_template.jinja2
```
:::

::: {.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.
:::

#### Content parts with per-part training control

Instead of using character offsets with `train_detail`, you can split a message's content into a list of parts, each with its own training flag. This is useful when you want to mask specific sections of a response (e.g., mask reasoning but train on the answer).

```{.json filename="data.jsonl"}
{
  "messages": [
    {"role": "user", "content": [{"type": "text", "text": "What is 2+2?"}]},
    {
      "role": "assistant",
      "content": [
        {"type": "text", "text": "Let me think step by step...", "train": false},
        {"type": "text", "text": " The answer is 4.", "train": true}
      ]
    }
  ]
}
```

The configuration is the same as standard `chat_template` — no extra fields needed:

```yaml
datasets:
  - path: ...
    type: chat_template
    roles_to_train: ["assistant"]
```

Each content part supports:

- `type`: `"text"` (required)
- `text`: the text value (also accepts `content` or `value` as the key)
- `train`: `true`/`false` (optional) — whether to train on this part
- `weight`: `0`/`1` (optional) — alternative to `train`

If a part has no `train` or `weight` flag, it inherits the turn-level training decision (from `roles_to_train`, `message_field_training`, or `train_on_inputs`).

::: {.callout-warning title="Whitespace at part boundaries"}
BPE tokenizers (used by Llama, Qwen, Mistral, GPT, etc.) prepend spaces to word tokens. For example, `" answer"` is a single token — the space is part of it. This means **where you place whitespace between content parts matters**:

**Split BEFORE spaces** (space goes with the next part):

```json
[
  {"type": "text", "text": "Let me think...", "train": false},
  {"type": "text", "text": " The answer is 4.", "train": true}
]
```

**DON'T put trailing spaces** on a part (the space merges with the next word into one token that straddles the boundary, and straddling tokens are masked):

```json
[
  {"type": "text", "text": "Let me think... ", "train": false},
  {"type": "text", "text": "The answer is 4.", "train": true}
]
```

In the bad example, `" The"` becomes a single token that spans both parts. Because it straddles the boundary, it is conservatively **masked** (not trained) — even though the second part has `train: true`.

**Newlines** typically merge with preceding punctuation (e.g., `":\n"` is one token). Keep newlines with the preceding part:

```json
[
  {"type": "text", "text": "Thinking:\n", "train": false},
  {"type": "text", "text": "The answer is 4.", "train": true}
]
```

Axolotl will log a warning if it detects trailing whitespace at a boundary between parts with different training flags.
:::

::: {.callout-note}
When all content parts in a message are strings, they are concatenated before being passed to the chat template. This means content parts work with **any** Jinja template — the template sees a plain string, and the per-part training flags are applied during tokenization.
:::

##### Per-part training on reasoning_content

For templates that support a separate `reasoning_content` field (e.g., `qwen3`), the same content-parts format works on `reasoning_content`. This is useful for masking incorrect reasoning steps while training on self-corrections:

```{.json filename="data.jsonl"}
{
  "messages": [
    {"role": "user", "content": [{"type": "text", "text": "What is 2+2?"}]},
    {
      "role": "assistant",
      "reasoning_content": [
        {"type": "text", "text": "Hmm maybe 2+2=5.", "train": false},
        {"type": "text", "text": " Wait no, 2+2=4.", "train": true}
      ],
      "content": [
        {"type": "text", "text": "The answer is 4.", "train": true}
      ]
    }
  ]
}
```

The `reasoning_content` and `content` fields are handled independently — each has its own token boundaries and per-part masking. No additional configuration is needed beyond what the template already requires.

::: {.callout-tip}
When `reasoning_content` is provided as a separate field, `split_thinking` is not needed — the reasoning is already separated from the content in the data.
:::

The same whitespace rules apply to `reasoning_content` parts as to `content` parts — split before spaces, keep newlines with the preceding part.


#### 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": "..."}]}
```