owl/camel/configs/vllm_config.py

# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from __future__ import annotations

from typing import Optional, Sequence, Union

from pydantic import Field

from camel.configs.base_config import BaseConfig
from camel.types import NOT_GIVEN, NotGiven


# flake8: noqa: E501
class VLLMConfig(BaseConfig):
    r"""Defines the parameters for generating chat completions using the
    OpenAI API.

    Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html

    Args:
        temperature (float, optional): Sampling temperature to use, between
            :obj:`0` and :obj:`2`. Higher values make the output more random,
            while lower values make it more focused and deterministic.
            (default: :obj:`0.2`)
        top_p (float, optional): An alternative to sampling with temperature,
            called nucleus sampling, where the model considers the results of
            the tokens with top_p probability mass. So :obj:`0.1` means only
            the tokens comprising the top 10% probability mass are considered.
            (default: :obj:`1.0`)
        n (int, optional): How many chat completion choices to generate for
            each input message. (default: :obj:`1`)
        response_format (object, optional): An object specifying the format
            that the model must output. Compatible with GPT-4 Turbo and all
            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
            {"type": "json_object"} enables JSON mode, which guarantees the
            message the model generates is valid JSON. Important: when using
            JSON mode, you must also instruct the model to produce JSON
            yourself via a system or user message. Without this, the model
            may generate an unending stream of whitespace until the generation
            reaches the token limit, resulting in a long-running and seemingly
            "stuck" request. Also note that the message content may be
            partially cut off if finish_reason="length", which indicates the
            generation exceeded max_tokens or the conversation exceeded the
            max context length.
        stream (bool, optional): If True, partial message deltas will be sent
            as data-only server-sent events as they become available.
            (default: :obj:`False`)
        stop (str or list, optional): Up to :obj:`4` sequences where the API
            will stop generating further tokens. (default: :obj:`None`)
        max_tokens (int, optional): The maximum number of tokens to generate
            in the chat completion. The total length of input tokens and
            generated tokens is limited by the model's context length.
            (default: :obj:`None`)
        presence_penalty (float, optional): Number between :obj:`-2.0` and
            :obj:`2.0`. Positive values penalize new tokens based on whether
            they appear in the text so far, increasing the model's likelihood
            to talk about new topics. See more information about frequency and
            presence penalties. (default: :obj:`0.0`)
        frequency_penalty (float, optional): Number between :obj:`-2.0` and
            :obj:`2.0`. Positive values penalize new tokens based on their
            existing frequency in the text so far, decreasing the model's
            likelihood to repeat the same line verbatim. See more information
            about frequency and presence penalties. (default: :obj:`0.0`)
        logit_bias (dict, optional): Modify the likelihood of specified tokens
            appearing in the completion. Accepts a json object that maps tokens
            (specified by their token ID in the tokenizer) to an associated
            bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
            is added to the logits generated by the model prior to sampling.
            The exact effect will vary per model, but values between:obj:` -1`
            and :obj:`1` should decrease or increase likelihood of selection;
            values like :obj:`-100` or :obj:`100` should result in a ban or
            exclusive selection of the relevant token. (default: :obj:`{}`)
        user (str, optional): A unique identifier representing your end-user,
            which can help OpenAI to monitor and detect abuse.
            (default: :obj:`""`)
        logprobs: Whether to return log probabilities of the output tokens or
            not. If true, returns the log probabilities of each output token
            returned in the `logits` of `message`. (default: :obj:`None`)
        top_logprobs: An integer between 0 and 20 specifying the number of
            most likely tokens to return at each token position, each with an
            associated log probability. `logprobs` must be set to `true` if
            this parameter is used. (default: :obj:`None`)
    """

    temperature: float = 0.2  # openai default: 1.0
    top_p: float = 1.0
    n: int = 1
    stream: bool = False
    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
    max_tokens: Union[int, NotGiven] = NOT_GIVEN
    presence_penalty: float = 0.0
    response_format: Union[dict, NotGiven] = NOT_GIVEN
    frequency_penalty: float = 0.0
    logit_bias: dict = Field(default_factory=dict)
    user: str = ""
    logprobs: Optional[bool] = None
    top_logprobs: Optional[int] = None


VLLM_API_PARAMS = {param for param in VLLMConfig.model_fields.keys()}