Hello I’m implementing a framework for fine-tuning various LLMs using the TRL library’s SFTTrainer. I have a question about how chat templates work:
When using SFTTrainer with datasets in the standard formats (with “messages” array or “prompt”/“completion” fields), does the trainer automatically apply the tokenizer’s chat_template? The documentation suggests it does.
For models whose tokenizers don’t have a chat_template attribute set (or it’s empty), what template does SFTTrainer apply by default? Is it using ChatML format?
For maximum performance, should I always manually set the appropriate chat_template on the tokenizer before passing it to SFTTrainer?
Just to be sure, I also asked Hugging Chat, and it seems to be okay. I think it probably works fairly well with the default settings.
The following is from Hugging Chat.
When using the SFTTrainer in the TRL library with datasets in standard formats (such as those with a “messages” array or “prompt”/“completion” fields), the trainer does automatically apply the tokenizer’s chat_template, according to the documentation [1][3][4].
This behavior is facilitated by the apply_chat_template method of the tokenizer, which is used to format the dataset into a structure suitable for training chat models. The setup is often handled using the setup_chat_format function from TRL, which configures the model and tokenizer with the necessary special tokens and formatting for conversational tasks [2][4].
If your dataset is in one of the supported standard formats, you can pass it directly to the SFTTrainer without pre-processing, and the trainer will handle the formatting for you [1][4].
When using the SFTTrainer and the tokenizer does not have a chat_template attribute set (or it is empty), the trainer does not automatically fall back to a default template like ChatML. Instead, the behavior depends on how the tokenizer is configured and whether you explicitly define a chat template for the model.
If the tokenizer does not have a chat_template attribute, the apply_chat_template method will either raise an error or fail to format the input, as it relies on the template being defined to structure the conversations appropriately [2]. This is because different models may use different chat formats during pre-training, and the chat template must match the model’s expected input format for optimal performance [2].
If you want to use a specific template (e.g., ChatML), you should explicitly set it as the chat_template in the tokenizer before using SFTTrainer. For example, you can define a ChatML template as follows:
tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
Once the chat_template is set, the SFTTrainer will use it to format the input data [2].
In summary:
If a chat_template is not set, the SFTTrainer will not automatically use a default template like ChatML.
You must explicitly define and set the chat_template for the tokenizer if one is not already provided.
If no template is defined, the apply_chat_template method will fail, as it requires a template to format the conversation data [2][1].
If you are training a model from scratch or fine-tuning it for chat, you have the flexibility to choose a template (e.g., ChatML) and configure it accordingly [2].
For maximum performance when using the SFTTrainer in the TRL library, it is highly recommended to manually set the appropriate chat_template on the tokenizer before initiating the training process. Here’s a structured overview of the reasoning and steps involved:
Why Manually Set the chat_template?
Consistency with Model Expectations: Different models are pre-trained on specific chat formats. Setting the correct chat_template ensures that the input data aligns with the model’s expected format, enhancing training effectiveness.
Special Tokens Handling: Many chat templates, such as ChatML, include special tokens (e.g., <|im_start|>). Ensuring these tokens are correctly formatted helps the model recognize and process them during training.
Avoiding Default Limitations: Relying on default settings can lead to suboptimal results if the tokenizer’s default template does not match your specific use case or model requirements.
Steps to Manually Set the chat_template
Choose the Right Template: Decide on the chat template format that best suits your model and task. Common formats include ChatML and Alpaca.
Define the Template: Create a Jinja template string that structures conversations. For instance, a ChatML template might look like:
chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
Apply the Template: Use the setup_chat_format function from the TRL library to apply the template to both the model and tokenizer.
from trl import setup_chat_format
model, tokenizer = setup_chat_format(model, tokenizer, chat_template=chat_template)
Initialize SFTTrainer: Pass the configured tokenizer and model to the SFTTrainer, ensuring the data collator and other parameters are set correctly.
Conclusion
Manually setting the chat_template is a crucial step for aligning the input data with the model’s expectations, especially for optimal performance in fine-tuning tasks. By defining the template explicitly, you ensure that the data is formatted correctly, include necessary special tokens, and thus maximize the effectiveness of the training process.