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https://github.com/BerriAI/litellm.git
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94 lines
No EOL
4.6 KiB
Python
94 lines
No EOL
4.6 KiB
Python
## Uses the huggingface text generation inference API
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import os, json
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from enum import Enum
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import requests
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from litellm import logging
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import time
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from typing import Callable
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class HuggingfaceError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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super().__init__(self.message) # Call the base class constructor with the parameters it needs
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class HuggingfaceRestAPILLM():
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def __init__(self, encoding, api_key=None) -> None:
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self.encoding = encoding
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self.validate_environment(api_key=api_key)
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def validate_environment(self, api_key): # set up the environment required to run the model
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self.headers = {
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"content-type": "application/json",
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}
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# get the api key if it exists in the environment or is passed in, but don't require it
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self.api_key = os.getenv("HF_TOKEN") if "HF_TOKEN" in os.environ else api_key
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if self.api_key != None:
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self.headers["Authorization"] = f"Bearer {self.api_key}"
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def completion(self, model: str, messages: list, custom_api_base: str, model_response: dict, print_verbose: Callable, optional_params=None, litellm_params=None, logger_fn=None): # logic for parsing in - calling - parsing out model completion calls
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if custom_api_base:
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completion_url = custom_api_base
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elif "HF_API_BASE" in os.environ:
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completion_url = os.getenv("HF_API_BASE")
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else:
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completion_url = f"https://api-inference.huggingface.co/models/{model}"
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prompt = ""
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if "meta-llama" in model and "chat" in model: # use the required special tokens for meta-llama - https://huggingface.co/blog/llama2#how-to-prompt-llama-2
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prompt = "<s>"
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for message in messages:
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if message["role"] == "system":
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prompt += "[INST] <<SYS>>" + message["content"]
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elif message["role"] == "assistant":
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prompt += message["content"] + "</s><s>[INST]"
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elif message["role"] == "user":
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prompt += message["content"] + "[/INST]"
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else:
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for message in messages:
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prompt += f"{message['content']}"
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### MAP INPUT PARAMS
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# max tokens
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if "max_tokens" in optional_params:
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value = optional_params.pop("max_tokens")
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optional_params["max_new_tokens"] = value
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data = {
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"inputs": prompt,
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# "parameters": optional_params
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}
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## LOGGING
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logging(model=model, input=prompt, additional_args={"litellm_params": litellm_params, "optional_params": optional_params}, logger_fn=logger_fn)
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## COMPLETION CALL
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response = requests.post(completion_url, headers=self.headers, data=json.dumps(data))
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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logging(model=model, input=prompt, additional_args={"litellm_params": litellm_params, "optional_params": optional_params, "original_response": response.text}, logger_fn=logger_fn)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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completion_response = response.json()
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print(f"response: {completion_response}")
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if isinstance(completion_response, dict) and "error" in completion_response:
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print(f"completion error: {completion_response['error']}")
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print(f"response.status_code: {response.status_code}")
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raise HuggingfaceError(message=completion_response["error"], status_code=response.status_code)
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else:
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model_response["choices"][0]["message"]["content"] = completion_response[0]["generated_text"]
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## CALCULATING USAGE
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prompt_tokens = len(self.encoding.encode(prompt)) ##[TODO] use the llama2 tokenizer here
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completion_tokens = len(self.encoding.encode(model_response["choices"][0]["message"]["content"])) ##[TODO] use the llama2 tokenizer here
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["usage"] = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens
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}
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return model_response
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pass
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def embedding(): # logic for parsing in - calling - parsing out model embedding calls
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pass |