Source code for camel.models.reward.nemotron_model

# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
import os
from typing import Dict, List, Optional, Union

from openai import OpenAI

from camel.models.reward import BaseRewardModel
from camel.types import ChatCompletion, ModelType
from camel.utils import api_keys_required


[docs] class NemotronRewardModel(BaseRewardModel): r"""Reward model based on the Nemotron model with OpenAI compatibility. Args: model_type (Union[ModelType, str]): Model for which a backend is created. api_key (Optional[str], optional): The API key for authenticating with the model service. (default: :obj:`None`) url (Optional[str], optional): The url to the model service. Note: The Nemotron model does not support model config. """ def __init__( self, model_type: Union[ModelType, str], api_key: Optional[str] = None, url: Optional[str] = None, ) -> None: url = url or os.environ.get( "NVIDIA_API_BASE_URL", "https://integrate.api.nvidia.com/v1" ) api_key = api_key or os.environ.get("NVIDIA_API_KEY") super().__init__(model_type, api_key, url) self._client = OpenAI( timeout=180, max_retries=3, base_url=self.url, api_key=self.api_key, )
[docs] @api_keys_required( [ (None, "NVIDIA_API_KEY"), ] ) def evaluate(self, messages: List[Dict[str, str]]) -> Dict[str, float]: r"""Evaluate the messages using the Nemotron model. Args: messages (List[Dict[str, str]]): A list of messages where each message is a dictionary format. Returns: Dict[str, float]: A dictionary mapping score types to their values. """ response = self._client.chat.completions.create( messages=messages, # type: ignore[arg-type] model=self.model_type, ) scores = self._parse_scores(response) return scores
[docs] def get_scores_types(self) -> List[str]: r"""Get the list of score types that the reward model can return. Returns: List[str]: A list of score types that the reward model can return. """ return [ "helpfulness", "correctness", "coherence", "complexity", "verbosity", ]
def _parse_scores(self, response: ChatCompletion) -> Dict[str, float]: r"""Parse the scores from the response. Args: response (ChatCompletion): A ChatCompletion object with the scores. Returns: Dict[str, float]: A dictionary mapping score types to their values. """ try: choices = response.choices logprobs = ( choices[0].logprobs.content if choices and choices[0].logprobs else None ) scores = ( {entry.token: entry.logprob for entry in logprobs if entry} if logprobs else {} ) return scores except Exception as e: raise ValueError(f"Failed to parse scores: {e}")