初始化项目，由ModelHub XC社区提供模型

Model: tzcfly/PertMind
Source: Original Platform

inference_transformers.py

@@ -0,0 +1,69 @@
+#!/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())