Conversation

Conversation format for supervised fine-tuning.

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.

data.jsonl

{"conversations": [{"role": "...", "content": "..."}]}

See configs for full configs and supported templates.

Migrating from sharegpt

Most configs can be adapted as follows:

# 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

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

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

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.

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

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

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.

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

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

# 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

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:.

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.

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)

Tip

See config documentation for detailed explanations of “turn”, “last”, and “all” options for training on tokens.

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 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.

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

datasets:
  - path: ...
    type: chat_template

    train_on_eos: last
    train_on_eot: turn

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.

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

For a data sample that looks like:

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:

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

Tip

It is not necessary to set both message_field_training and message_field_training_detail at once.

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

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

For example, a content can look like:

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

After split, it will look like:

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

sharegpt

Important

ShareGPT is deprecated!. Please see chat_template section.

pygmalion

data.jsonl

{"conversations": [{"role": "...", "value": "..."}]}