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feat(doc): updated config with chat template options and clarified examples

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NanoCode012
2024-10-10 17:57:11 +07:00
parent 203ae28704
commit 6b3cdfdb8e
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43
docs/config.qmd
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@@ -83,7 +83,7 @@ lora_on_cpu: true
datasets:
# HuggingFace dataset repo | s3://,gs:// path | "json" for local dataset, make sure to fill data_files
- path: vicgalle/alpaca-gpt4
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
# The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
ds_type: # Optional[str] (json|arrow|parquet|text|csv) defines the datatype when path is a file
data_files: # Optional[str] path to source data files
@@ -123,6 +123,47 @@ datasets:
# For `completion` datsets only, uses the provided field instead of `text` column
field:
# Using chat template
- path: ...
# Set type to `chat_template` to use this strategy
type: chat_template
# Specify the name of the chat template to use
# The name of the chat template to use for training, following values are supported:
# - 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.
# - 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
# - tokenizer_default_fallback_*: where * is the name of the chat template to fallback to if the tokenizer does not have a chat template else default to tokenizer. E.g. tokenizer_default_fallback_chatml.
# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
chat_template: tokenizer_default
# Custom jinja template for chat template. This will be only used if `chat_template` is set to `jinja` or empty (in which case chat_template is automatically set to `jinja`).
chat_template_jinja:
# The key in the data example that contains the messages. Default is "messages".
field_messages: messages
# The key in the message turn that contains the role. Default is "role".
message_field_role: role
# The key in the message turn that contains the content. Default is "content".
message_field_content: content
# Optional[Dict[str, List]]. Roles mapping for the messages.
roles:
user: ["human", "user"]
assistant: ["gpt", "assistant", "ai"]
system: ["system"]
## NOTE: Leaving the below empty will default to using the simple legacy tokenization strategy where only last message is trained on.
# Optional[List[str]]. Roles to train on. The tokens from these roles will be considered for the loss.
roles_to_train: ["gpt", "assistant"]
# Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
# - all: train on all EOS tokens
# - turn: train on the EOS token at the end of each trainable turn
# - last: train on the last EOS token in the conversation
train_on_eos: last
# The key in the message turn that indicates via boolean whether tokens of a turn should be considered for training. Useful to selectively train on certain turns besides the `roles_to_train`.
message_field_training: training
# The key in the message turn that contains the training details. Useful to selectively train on certain tokens in a turn.
# The value of the key is a List[Dict] containing `begin_offset` (start character index in content), `end_offset` (end character index in content), and `train` (boolean whether to train).
message_field_training_detail: train_detail
# If false, the datasets will not be shuffled and will keep their original order in `datasets`.
# The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
shuffle_merged_datasets: true

93
docs/dataset-formats/conversation.qmd
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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
## chat_template
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`.
Conversational data would normally look like follows:
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"}
{"conversations": [{"from": "...", "value": "..."}]}
{"conversations": [{"role": "...", "content": "..."}]}
```
with roles usually being system, user, assistant, etc.
However, all fields can be customized using the following configuration:
```yaml
datasets:
- path: ...
# Set type to `chat_template` to use this strategy
type: chat_template
# Specify the name of the chat template to use
# The name of the chat template to use for training, following values are supported:
# - 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.
# - 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
# - 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.
# - jinja: Uses a custom jinja template for the chat template. The custom jinja template should be provided in the chat_template_jinja field.
chat_template: tokenizer_default
# 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.
chat_template_jinja: null
# The key in the data example that contains the messages. Default is "conversations".
field_messages: conversations
# The key in the message turn that contains the role. Default is "from".
message_field_role: from
# The key in the message turn that contains the content. Default is "value".
message_field_content: value
# Role mapping for the messages. This can be useful if you are combining data from multiple sources and the roles are different.
roles:
human: user
user: user
assistant: assistant
gpt: assistant
system: system
# Roles to train on. The tokens from these roles will be considered for the loss. Default is ["gpt", "assistant"]
roles_to_train: ["gpt", "assistant"]
# Which EOS tokens to train on in the conversation. Possible values are:
# - all: train on all EOS tokens
# - turn: train on the EOS token at the end of each trainable turn
# - last: train on the last EOS token in the conversation
# - none: do not train on EOS tokens
# Default is "turn".
train_on_eos: turn
# 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".
message_field_training: training
# 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".
message_field_training_detail: train_detail
```
See `config.qmd` for full configs and supported templates.
### Examples
1. Using the default chat template in the tokenizer_config.json on OpenAI messages format
1. 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
chat_template: tokenizer_default
```
2. Using the default chat template in the tokenizer_config.json on OpenAI messages format, training on all assistant messages.
```yaml
datasets:
- path: ...
type: chat_template
chat_template: tokenizer_default
field_messages: messages
message_field_role: role
message_field_content: content
roles:
user: user
assistant: assistant
human: user
gpt: assistant
system: system
roles_to_train: ["assistant"]
```
2. Using a custom jinja template on OpenAI messages format
3. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
```yaml
datasets:
@@ -155,20 +110,10 @@ datasets:
type: chat_template
chat_template: jinja
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 %}"
field_messages: messages
message_field_role: role
message_field_content: content
roles:
user: user
assistant: assistant
human: user
gpt: assistant
system: system
roles_to_train: ["assistant"]
```
3. Using fine-grained control over tokens and turns to train in a conversation
4. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
For a data sample that looks like:
@@ -207,14 +152,10 @@ datasets:
field_messages: conversations
message_field_role: from
message_field_content: value
roles:
human: human
user: human
assistant: assistant
gpt: assistant
system: system
roles_to_train: []
train_on_eos: turn
message_field_training: train
message_field_training_detail: train_detail
```
Tip: It is not necessary to use both `message_field_training` and `message_field_training_detail` at a time.