| import os |
| from time import sleep |
|
|
| try: |
| import openai |
| from openai import OpenAI |
| except ImportError as e: |
| pass |
|
|
| from lcb_runner.runner.base_runner import BaseRunner |
|
|
|
|
| class DeepSeekRunner(BaseRunner): |
| client = OpenAI( |
| api_key=os.getenv("DEEPSEEK_API"), |
| base_url="https://api.deepseek.com", |
| ) |
|
|
| def __init__(self, args, model): |
| super().__init__(args, model) |
| self.client_kwargs: dict[str | str] = { |
| "model": args.model, |
| "temperature": args.temperature, |
| "max_tokens": args.max_tokens, |
| "top_p": args.top_p, |
| "frequency_penalty": 0, |
| "presence_penalty": 0, |
| "n": 1, |
| "timeout": args.openai_timeout, |
| |
| } |
|
|
| def _run_single(self, prompt: list[dict[str, str]]) -> list[str]: |
| assert isinstance(prompt, list) |
|
|
| def __run_single(counter): |
| try: |
| response = self.client.chat.completions.create( |
| messages=prompt, |
| **self.client_kwargs, |
| ) |
| content = response.choices[0].message.content |
| return content |
| except ( |
| openai.APIError, |
| openai.RateLimitError, |
| openai.InternalServerError, |
| openai.OpenAIError, |
| openai.APIStatusError, |
| openai.APITimeoutError, |
| openai.InternalServerError, |
| openai.APIConnectionError, |
| ) as e: |
| print("Exception: ", repr(e)) |
| print("Sleeping for 30 seconds...") |
| print("Consider reducing the number of parallel processes.") |
| sleep(30) |
| return DeepSeekRunner._run_single(prompt) |
| except Exception as e: |
| print(f"Failed to run the model for {prompt}!") |
| print("Exception: ", repr(e)) |
| raise e |
|
|
| outputs = [] |
| try: |
| for _ in range(self.args.n): |
| outputs.append(__run_single(10)) |
| except Exception as e: |
| raise e |
| return outputs |
|
|
