File size: 2,458 Bytes
14a19cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | #!/usr/bin/env python3
"""Run PertMind with Hugging Face Transformers."""
from __future__ import annotations
import argparse
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
DEFAULT_SYSTEM_PROMPT = (
"You are PertMind, a biomedical assistant. For biomedical prediction, "
"screen-ranking, or gene-set interpretation tasks, answer first and then "
"provide a concise explanation. Use this style when applicable:\n"
"Final Answer: <answer>\nExplanation: <brief explanation>"
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model", default=".", help="Path or Hugging Face model id.")
parser.add_argument("--prompt", required=True, help="User prompt.")
parser.add_argument("--system-prompt", default=DEFAULT_SYSTEM_PROMPT)
parser.add_argument("--max-new-tokens", type=int, default=768)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--top-p", type=float, default=0.95)
return parser.parse_args()
def main() -> int:
args = parse_args()
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
args.model,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
trust_remote_code=True,
)
messages = [
{"role": "system", "content": args.system_prompt},
{"role": "user", "content": args.prompt},
]
try:
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
except TypeError:
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
do_sample = args.temperature > 0
outputs = model.generate(
**inputs,
max_new_tokens=args.max_new_tokens,
do_sample=do_sample,
temperature=args.temperature if do_sample else None,
top_p=args.top_p if do_sample else None,
pad_token_id=tokenizer.eos_token_id,
)
generated = outputs[0, inputs["input_ids"].shape[-1] :]
print(tokenizer.decode(generated, skip_special_tokens=True).strip())
return 0
if __name__ == "__main__":
raise SystemExit(main())
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