mirror of
https://github.com/BerriAI/litellm.git
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c93364ab2e
* add max_completion_tokens * add max_completion_tokens * add max_completion_tokens support for OpenAI models * add max_completion_tokens param * add max_completion_tokens for bedrock converse models * add test for converse maxTokens * fix openai o1 param mapping test * move test optional params * add max_completion_tokens for anthropic api * fix conftest * add max_completion tokens for vertex ai partner models * add max_completion_tokens for fireworks ai * add max_completion_tokens for hf rest api * add test for param mapping * add param mapping for vertex, gemini + testing * predibase is the most unstable and unusable llm api in prod, can't handle our ci/cd * add max_completion_tokens to openai supported params * fix fireworks ai param mapping
143 lines
4.7 KiB
Python
143 lines
4.7 KiB
Python
"""
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Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
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This is OpenAI compatible
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This file only contains param mapping logic
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API calling is done using the OpenAI SDK with an api_base
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"""
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import types
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from typing import Optional, Union
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class NvidiaNimConfig:
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"""
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Reference: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
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The class `NvidiaNimConfig` provides configuration for the Nvidia NIM's Chat Completions API interface. Below are the parameters:
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"""
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temperature: Optional[int] = None
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top_p: Optional[int] = None
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frequency_penalty: Optional[int] = None
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presence_penalty: Optional[int] = None
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max_tokens: Optional[int] = None
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stop: Optional[Union[str, list]] = None
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def __init__(
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self,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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frequency_penalty: Optional[int] = None,
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presence_penalty: Optional[int] = None,
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max_tokens: Optional[int] = None,
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stop: Optional[Union[str, list]] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Get the supported OpenAI params for the given model
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Updated on July 5th, 2024 - based on https://docs.api.nvidia.com/nim/reference
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"""
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if model in [
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"google/recurrentgemma-2b",
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"google/gemma-2-27b-it",
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"google/gemma-2-9b-it",
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"gemma-2-9b-it",
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]:
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return ["stream", "temperature", "top_p", "max_tokens", "stop", "seed"]
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elif model == "nvidia/nemotron-4-340b-instruct":
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return [
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"stream",
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"temperature",
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"top_p",
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"max_tokens",
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"max_completion_tokens",
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]
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elif model == "nvidia/nemotron-4-340b-reward":
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return [
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"stream",
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]
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elif model in ["google/codegemma-1.1-7b"]:
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# most params - but no 'seed' :(
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return [
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"stream",
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"temperature",
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"top_p",
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"frequency_penalty",
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"presence_penalty",
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"max_tokens",
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"max_completion_tokens",
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"stop",
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]
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else:
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# DEFAULT Case - The vast majority of Nvidia NIM Models lie here
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# "upstage/solar-10.7b-instruct",
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# "snowflake/arctic",
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# "seallms/seallm-7b-v2.5",
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# "nvidia/llama3-chatqa-1.5-8b",
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# "nvidia/llama3-chatqa-1.5-70b",
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# "mistralai/mistral-large",
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# "mistralai/mixtral-8x22b-instruct-v0.1",
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# "mistralai/mixtral-8x7b-instruct-v0.1",
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# "mistralai/mistral-7b-instruct-v0.3",
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# "mistralai/mistral-7b-instruct-v0.2",
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# "mistralai/codestral-22b-instruct-v0.1",
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# "microsoft/phi-3-small-8k-instruct",
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# "microsoft/phi-3-small-128k-instruct",
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# "microsoft/phi-3-mini-4k-instruct",
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# "microsoft/phi-3-mini-128k-instruct",
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# "microsoft/phi-3-medium-4k-instruct",
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# "microsoft/phi-3-medium-128k-instruct",
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# "meta/llama3-70b-instruct",
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# "meta/llama3-8b-instruct",
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# "meta/llama2-70b",
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# "meta/codellama-70b",
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return [
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"stream",
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"temperature",
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"top_p",
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"frequency_penalty",
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"presence_penalty",
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"max_tokens",
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"max_completion_tokens",
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"stop",
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"seed",
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]
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def map_openai_params(
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self, model: str, non_default_params: dict, optional_params: dict
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) -> dict:
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supported_openai_params = self.get_supported_openai_params(model=model)
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for param, value in non_default_params.items():
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if param == "max_completion_tokens":
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optional_params["max_tokens"] = value
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elif param in supported_openai_params:
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optional_params[param] = value
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return optional_params
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