nat.atif.metrics

Per-step metrics model for ATIF trajectories.

Classes

Metrics

LLM operational and confidence data for a single step.

Module Contents

class Metrics(/, **data: Any)

Bases: pydantic.BaseModel

LLM operational and confidence data for a single step.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

prompt_tokens: int | None = None

completion_tokens: int | None = None

cached_tokens: int | None = None

cost_usd: float | None = None

prompt_token_ids: list[int] | None = None

completion_token_ids: list[int] | None = None

logprobs: list[float] | None = None

extra: dict[str, Any] | None = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].