ca796fb56e
* feat(doc): update gpt-oss readme * fix: caps * feat: add toolcalling section * feat: add example tool dataset to docs * chore: update
323 lines
9.0 KiB
Plaintext
323 lines
9.0 KiB
Plaintext
---
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title: Conversation
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description: Conversation format for supervised fine-tuning.
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order: 3
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---
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## chat_template
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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.
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```{.json filename="data.jsonl"}
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{"messages": [{"role": "...", "content": "..."}, {"role": "...", "content": "..."}, ...]}
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```
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See [configs](../config-reference.qmd) for full configs and supported templates.
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### Migrating from sharegpt
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Most configs can be adapted as follows:
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```yaml
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# old
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chat_template: chatml
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datasets:
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- path: ...
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type: sharegpt
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conversation: chatml
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# new (if using tokenizer's chat_template)
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datasets:
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- path: ...
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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# new (if setting a new chat_template like chatml, gemma, etc)
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chat_template: chatml
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datasets:
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- path: ...
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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```
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We recommend checking the below examples for other usecases.
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### Examples
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#### Training on last message
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(Legacy) Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on only last message.
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```yaml
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datasets:
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- path: ...
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type: chat_template
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roles_to_train:
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train_on_eos:
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```
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::: {.callout-tip}
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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`.
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:::
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#### Overriding default chat template
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Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
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```yaml
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chat_template: gemma # this overwrites the tokenizer's chat_template
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datasets:
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- path: ...
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type: chat_template
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roles_to_train: ["assistant"] # default value
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```
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::: {.callout-note}
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If you want to use built-in chat_template, use `chat_template: tokenizer_default` (this is set by default).
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:::
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#### Using default chat template with fallback
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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.
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```yaml
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chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
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datasets:
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- path: ...
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type: chat_template
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```
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#### Custom Jinja template
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Using a custom jinja template on OpenAI messages format, training on all assistant messages.
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```yaml
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# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
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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 %}"
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datasets:
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- path: ...
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type: chat_template
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```
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::: {.callout-important}
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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: `.
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:::
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#### Using template with different token for EOT and EOS
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- 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.
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```yaml
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eot_tokens:
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- "[/INST]"
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# - "[/SYSTEM_PROMPT]"
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datasets:
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- path: ...
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type: chat_template
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# optional
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train_on_eot: turn # defaults read from train_on_eos (which defaults to turn)
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```
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::: {.callout-tip}
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See [config documentation](../config-reference.qmd) for detailed explanations of "turn", "last", and "all" options for training on tokens.
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:::
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::: {.callout-note}
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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.
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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.
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:::
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- 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`.
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```yaml
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eot_tokens:
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- "[/INST]"
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# ...
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datasets:
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- path: ...
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type: chat_template
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train_on_eos: last
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train_on_eot: turn
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```
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::: {.callout-tip}
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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.
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:::
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#### Using tool use
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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.
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```json
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{
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"tools": [
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{
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"type": "...",
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"function": {
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"name": "...",
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"description": "...",
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"parameters": {
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"type": "...",
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"properties": {
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// ...
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},
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"required": ["..."],
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},
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},
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},
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],
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"messages": [
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// ...
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{
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"role": "assistant", // call the function via assistant
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"tool_calls": [
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{
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"id": "...", // required only for mistral
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"type": "function",
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"function": {
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"name": "...",
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"arguments": {
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"...": "...",
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}
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}
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}
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]
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},
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{
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"role": "tool",
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"tool_call_id": "...", // required only for mistral
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"name": "...",
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"content": "..."
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},
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],
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}
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```
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::: {.callout-note}
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Tools need to follow [JSON schema](https://json-schema.org/learn/getting-started-step-by-step).
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:::
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Example config for Llama4:
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```yaml
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chat_template: llama4
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datasets:
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- path: Nanobit/text-tools-2k-test
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type: chat_template
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# field_tools: tools # default is `tools`
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```
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::: {.callout-tip}
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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.
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:::
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#### Using fine-grained control over token masking
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(Advanced) Using fine-grained control over tokens and turns to train in a conversation
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For a data sample that looks like:
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```{.json filename="data.jsonl"}
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{
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"conversations": [
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{"from": "system", "value": "You are an AI assistant.", "train": false},
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{"from": "human", "value": "Hello", "train": false},
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{"from": "assistant", "value": "Hello", "train": true},
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{"from": "human", "value": "How are you?", "train": true},
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{
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"from": "assistant",
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"value": "I'm doing very well, thank you!",
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"train_detail": [
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{"begin_offset": 0, "end_offset": 8, "train": false},
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{"begin_offset": 9, "end_offset": 18, "train": true},
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{"begin_offset": 19, "end_offset": 30, "train": false},
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],
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},
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{
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"from": "human",
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"value": "I'm doing very well, thank you!",
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"train": true,
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},
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{"from": "assistant", "value": "Hi there!", "train": true}
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]
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}
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```
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The configuration would look like:
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```yaml
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datasets:
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- path: ...
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type: chat_template
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chat_template: tokenizer_default
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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roles_to_train: []
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train_on_eos: turn
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message_field_training: train
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message_field_training_detail: train_detail
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```
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::: {.callout-tip}
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It is not necessary to set both `message_field_training` and `message_field_training_detail` at once.
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:::
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#### Reasoning split
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(For Qwen3 template only) Enable reasoning split, where the reasoning is split from the content and passed as a separate field into the template.
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```yaml
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datasets:
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- path: ...
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type: chat_template
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chat_template: qwen3
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split_thinking: true
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```
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For example, a content can look like:
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```json
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{
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"content": "<think>Some thinking outputs</think>Output after thinking."
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}
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```
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After split, it will look like:
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```json
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{
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"reasoning_content": "Some thinking outputs",
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"content": "Output after thinking..."
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}
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```
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## sharegpt
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::: {.callout-important}
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ShareGPT is deprecated!. Please see [chat_template](#chat_template) section.
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:::
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## pygmalion
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```{.json filename="data.jsonl"}
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{"conversations": [{"role": "...", "value": "..."}]}
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```
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