adding log_prob option for chat models #219
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Review by Korbit AI
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| src/agentlab/llm/base_api.py | ✅ |
| src/agentlab/llm/huggingface_utils.py | ✅ |
| src/agentlab/llm/chat_api.py | ✅ |
| src/agentlab/llm/llm_utils.py | ✅ |
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| res = AIMessage(completion.choices[0].message.content) | ||
| if self.log_probs: | ||
| res["log_probs"] = completion.choices[0].log_probs |
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src/agentlab/llm/llm_utils.py
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| def __init__(self, role: str, content: Union[str, list[dict]], **kwargs): | ||
| self["role"] = role | ||
| self["content"] = deepcopy(content) | ||
| self.update(kwargs) |
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TLSDC
commented
Feb 18, 2025
| @@ -21,6 +21,7 @@ class BaseModelArgs(ABC): | |||
| max_new_tokens: int = None | |||
| temperature: float = 0.1 | |||
| vision_support: bool = False | |||
| log_probs: bool = False | |||
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The log_probs argument is now part of all chat_model_args, and has to be set to True in your llm config @optimass
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With this option on (in LLM configs), the chat_messages will also save log-probabilities that will be saved along with the outputs.
Description by Korbit AI
What change is being made?
Add support for logging probability (
log_probs) in chat models by introducing alog_probsoption across various components of the chat model architecture.Why are these changes being made?
To allow users to obtain the probabilities associated with model predictions, providing insight into model confidence and improving model interpretability. This change enhances the flexibility and functionality of the chat model by giving users the option to access additional predictive information if desired.