feat(doc): updated config with chat template options and clarified examples
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@@ -73,81 +73,36 @@ creates a chat where bot is asked to tell a joke, then explain why the joke is f
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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. Usually this chat template is stored in tokenizer_config.json under the key `chat_template`.
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Conversational data would normally look like follows:
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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": [{"from": "...", "value": "..."}]}
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{"conversations": [{"role": "...", "content": "..."}]}
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```
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with roles usually being system, user, assistant, etc.
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However, all fields can be customized using the following configuration:
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```yaml
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datasets:
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- path: ...
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# Set type to `chat_template` to use this strategy
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type: chat_template
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# Specify the name of the chat template to use
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# The name of the chat template to use for training, following values are supported:
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# - tokenizer_default: Uses the chat template that is available in the tokenizer_config.json. If the chat template is not available in the tokenizer, it will raise an error. This is the default value.
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# - alpaca/inst/chatml/gemma/cohere/llama3/phi_3/deepseek_v2/jamba: These chat templates are available in the axolotl codebase at src/axolotl/utils/chat_templates.py
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# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to. E.g. tokenizer_default_fallback_chatml. This is useful when the chat template is not available in the tokenizer.
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# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
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chat_template: tokenizer_default
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# custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
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chat_template_jinja: null
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# The key in the data example that contains the messages. Default is "conversations".
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field_messages: conversations
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# The key in the message turn that contains the role. Default is "from".
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message_field_role: from
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# The key in the message turn that contains the content. Default is "value".
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message_field_content: value
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# Role mapping for the messages. This can be useful if you are combining data from multiple sources and the roles are different.
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roles:
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human: user
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user: user
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assistant: assistant
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gpt: assistant
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system: system
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# Roles to train on. The tokens from these roles will be considered for the loss. Default is ["gpt", "assistant"]
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roles_to_train: ["gpt", "assistant"]
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# Which EOS tokens to train on in the conversation. Possible values are:
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# - all: train on all EOS tokens
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# - turn: train on the EOS token at the end of each trainable turn
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# - last: train on the last EOS token in the conversation
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# - none: do not train on EOS tokens
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# Default is "turn".
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train_on_eos: turn
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# The key in the message turn that indicates if tokens of a turn should be considered for training. This is an advanced option useful to selectively train on certain turns besides the `roles_to_train`. Default is "training".
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message_field_training: training
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# The key in the message turn that contains the training details. This is an advanced option useful to selectively train on certain tokens in a turn. Default is "train_detail".
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message_field_training_detail: train_detail
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```
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See `config.qmd` for full configs and supported templates.
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### Examples
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1. Using the default chat template in the tokenizer_config.json on OpenAI messages format
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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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chat_template: tokenizer_default
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```
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2. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on all assistant messages.
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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: messages
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message_field_role: role
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message_field_content: content
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roles:
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user: user
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assistant: assistant
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human: user
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gpt: assistant
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system: system
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roles_to_train: ["assistant"]
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```
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2. Using a custom jinja template on OpenAI messages format
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3. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
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```yaml
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datasets:
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@@ -155,20 +110,10 @@ datasets:
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type: chat_template
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chat_template: jinja
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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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field_messages: messages
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message_field_role: role
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message_field_content: content
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roles:
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user: user
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assistant: assistant
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human: user
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gpt: assistant
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system: system
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roles_to_train: ["assistant"]
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```
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3. Using fine-grained control over tokens and turns to train in a conversation
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4. (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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@@ -207,14 +152,10 @@ datasets:
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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:
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human: human
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user: human
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assistant: assistant
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gpt: assistant
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system: system
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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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