Template

The template is a key component for converting Trajectory to InputFeature.

class Template:

    def __init__(self,
                 model_id: str,
                 use_chat_template: bool = True,
                 max_length: Optional[int] = 8192,
                 truncation_strategy: Literal['raise', 'left', 'right', 'split'] = 'raise',
                 default_system: Optional[str] = None):
        ...

    def batch_encode(self, trajectories: Union[Dict[str, Any], List[Trajectory]]) -> List[InputFeature]:
        # Batch encode samples
        ...

    def check(self, trajectory: Trajectory) -> Optional[Trajectory]:
        # Encode one sample and return the original sample
        # Generally used to check data reasonableness in RL algorithms like GRPO
        ...

    def batch_check(self, trajectories: List[Trajectory]) -> List[Optional[Trajectory]]:
        # Batch check samples
        ...

    def decode(self, token_ids: List[int], **kwargs) -> str:
        # Decode sample
        ...

    def batch_decode(self, token_ids: List[List[int]], **kwargs) -> List[str]:
        # Batch decode samples
        ...

• model_id: Model id containing tokenizer or processor

• use_chat_template: Whether to use chat_template. If not used, it is generally a pre-training scenario

• max_length: Maximum length of a single sample

• truncation_strategy: How to handle the sample if it exceeds the maximum length

  • raise: Throw an exception. Generally used for very precise dataset scenarios

  • left: Remove tokens on the left to conform to max_length

  • right: Remove tokens on the right to conform to max_length

  • default_system: If the dataset does not have a system, use the default system

Template does not support using functions as replacements because it needs to support many functions internally. If you need to write a new Template, please inherit the Template class. Generally speaking, using the Template base class is sufficient for pure text models. In the base class, we use tokenizer.apply_chat_template to encode the model, which is universal for general pure text models.

Template mapping

Currently, the model-template mapping is simple:

• Template class：Supported in all pure text LLMs.

• Qwen3_5Template class: For Qwen3.5 MLLMs.