初始化项目，由ModelHub XC社区提供模型

Model: tzcfly/PertMind
Source: Original Platform

inference_vllm.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Run PertMind with vLLM."""
+
+from __future__ import annotations
+
+import argparse
+
+from vllm import LLM, SamplingParams
+
+
+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-tokens", type=int, default=768)
+    parser.add_argument("--temperature", type=float, default=0.0)
+    parser.add_argument("--top-p", type=float, default=0.95)
+    parser.add_argument("--max-model-len", type=int, default=12288)
+    parser.add_argument("--gpu-memory-utilization", type=float, default=0.85)
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    llm = LLM(
+        model=args.model,
+        trust_remote_code=True,
+        dtype="bfloat16",
+        max_model_len=args.max_model_len,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+    )
+    tokenizer = llm.get_tokenizer()
+    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)
+    params = SamplingParams(
+        temperature=args.temperature,
+        top_p=args.top_p,
+        max_tokens=args.max_tokens,
+    )
+    output = llm.generate([text], params)[0].outputs[0].text.strip()
+    print(output)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())