67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
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#!/usr/bin/env python3
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"""Run PertMind with vLLM."""
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from __future__ import annotations
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import argparse
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from vllm import LLM, SamplingParams
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DEFAULT_SYSTEM_PROMPT = (
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"You are PertMind, a biomedical assistant. For biomedical prediction, "
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"screen-ranking, or gene-set interpretation tasks, answer first and then "
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"provide a concise explanation. Use this style when applicable:\n"
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"Final Answer: <answer>\nExplanation: <brief explanation>"
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model", default=".", help="Path or Hugging Face model id.")
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parser.add_argument("--prompt", required=True, help="User prompt.")
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parser.add_argument("--system-prompt", default=DEFAULT_SYSTEM_PROMPT)
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parser.add_argument("--max-tokens", type=int, default=768)
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parser.add_argument("--temperature", type=float, default=0.0)
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parser.add_argument("--top-p", type=float, default=0.95)
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parser.add_argument("--max-model-len", type=int, default=12288)
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parser.add_argument("--gpu-memory-utilization", type=float, default=0.85)
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return parser.parse_args()
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def main() -> int:
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args = parse_args()
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llm = LLM(
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model=args.model,
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trust_remote_code=True,
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dtype="bfloat16",
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max_model_len=args.max_model_len,
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gpu_memory_utilization=args.gpu_memory_utilization,
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)
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tokenizer = llm.get_tokenizer()
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messages = [
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{"role": "system", "content": args.system_prompt},
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{"role": "user", "content": args.prompt},
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]
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try:
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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except TypeError:
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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params = SamplingParams(
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temperature=args.temperature,
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top_p=args.top_p,
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max_tokens=args.max_tokens,
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)
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output = llm.generate([text], params)[0].outputs[0].text.strip()
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print(output)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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