litellm-mirror/litellm/llms/nvidia_nim.py
2024-06-25 09:13:08 -07:00

79 lines
2.3 KiB
Python

"""
Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
This is OpenAI compatible
This file only contains param mapping logic
API calling is done using the OpenAI SDK with an api_base
"""
import types
from typing import Optional, Union
class NvidiaNimConfig:
"""
Reference: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
The class `NvidiaNimConfig` provides configuration for the Nvidia NIM's Chat Completions API interface. Below are the parameters:
"""
temperature: Optional[int] = None
top_p: Optional[int] = None
frequency_penalty: Optional[int] = None
presence_penalty: Optional[int] = None
max_tokens: Optional[int] = None
stop: Optional[Union[str, list]] = None
def __init__(
self,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
frequency_penalty: Optional[int] = None,
presence_penalty: Optional[int] = None,
max_tokens: Optional[int] = None,
stop: Optional[Union[str, list]] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
def get_supported_openai_params(self):
return [
"stream",
"temperature",
"top_p",
"frequency_penalty",
"presence_penalty",
"max_tokens",
"stop",
]
def map_openai_params(
self, non_default_params: dict, optional_params: dict
) -> dict:
supported_openai_params = self.get_supported_openai_params()
for param, value in non_default_params.items():
if param in supported_openai_params:
optional_params[param] = value
return optional_params