fd3b80716a
* remove fastchat and sharegpt * remove imports * remove more fastchat imports * chore: remove unused functions * feat: remove sharegpt and deprecate from docs * chore: remove unused sharegpt checks * fix: remove sharegpt type from tests * feat: add sharegpt deprecation error * feat: update readme --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
152 lines
4.2 KiB
Plaintext
152 lines
4.2 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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## sharegpt
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IMPORTANT: ShareGPT is deprecated!. Please see `chat_template` section below.
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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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## 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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{"conversations": [{"role": "...", "content": "..."}]}
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```
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See `config.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_field_role: from
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message_field_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_field_role: from
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message_field_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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1. 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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```
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2. 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"]
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```
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3. 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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roles_to_train: ["assistant"]
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```
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4. 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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roles_to_train: ["assistant"]
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```
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5. (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_field_role: from
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message_field_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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Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.
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