初始化项目，由ModelHub XC社区提供模型

Model: sabari2005/cyberslm-instruct
Source: Original Platform

infer_chat.py

@@ -0,0 +1,118 @@
+"""
+Instruction-tuned model inference — question answering.
+
+    python Final/infer_chat.py --prompt "What is SQL injection?"
+    python Final/infer_chat.py --interactive
+
+The prompt is built with the SAME formatter used during fine-tuning, so the
+model sees exactly the token sequence it was trained on. Hand-assembling the
+prompt string instead produces different token ids at every segment boundary
+(SentencePiece prepends a word-start marker per encode call) and the model then
+sees something it was never trained on.
+
+Expect correctly-shaped answers with unreliable facts: this is a 33.5M-parameter
+model. See runs/reports/FINAL_REPORT.md for measured behaviour.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import torch
+
+_HERE = Path(__file__).resolve().parent
+for _p in (_HERE, _HERE / "cyberslm_sft"):
+    if str(_p) not in sys.path:
+        sys.path.insert(0, str(_p))
+
+from configs.sft_config import default_config                      # noqa: E402
+from data.prompt_formatter import PromptFormatter, Tokenizer       # noqa: E402
+from model.cyberslm import CyberSLM as SFTModel                    # noqa: E402
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="CyberSLM instruct (question answering)")
+    ap.add_argument("--prompt", "-p", default="What is SQL injection and how do I prevent it?")
+    ap.add_argument("--interactive", "-i", action="store_true")
+    ap.add_argument("--checkpoint", "-c", default=str(_HERE / "models" / "instruct.pt"))
+    ap.add_argument("--tokenizer", default=str(_HERE / "tokenizer" / "tokenizer.model"))
+    ap.add_argument("--max-new-tokens", "-m", type=int, default=200)
+    ap.add_argument("--temperature", "-t", type=float, default=0.0,
+                    help="0 = greedy/deterministic (recommended for this model)")
+    ap.add_argument("--top-k", type=int, default=50)
+    ap.add_argument("--top-p", type=float, default=0.9)
+    ap.add_argument("--repetition-penalty", type=float, default=1.1)
+    ap.add_argument("--device", default=None)
+    args = ap.parse_args()
+
+    device = torch.device(args.device) if args.device else torch.device(
+        "cuda" if torch.cuda.is_available() else "cpu")
+
+    ckpt = Path(args.checkpoint)
+    if not ckpt.exists():
+        print(f"Checkpoint not found: {ckpt}", file=sys.stderr)
+        return 1
+
+    cfg = default_config()
+    cfg.tokenizer.model_path = args.tokenizer
+    cfg.model.max_seq_len = 2048
+    cfg.data.max_seq_len = 2048
+
+    tok = Tokenizer(cfg.tokenizer.model_path)
+    fmt = PromptFormatter(cfg=cfg, tokenizer=tok)
+
+    model = SFTModel(cfg.model)
+    state = torch.load(ckpt, map_location=device, weights_only=False)
+    if isinstance(state, dict) and "model_state" in state:
+        state = state["model_state"]
+    model.load_state_dict(state)
+    model.to(device).eval()
+
+    n = sum(p.numel() for p in model.parameters())
+    print(f"model  : {ckpt.name}  ({n:,} params)")
+    print(f"context: {cfg.model.max_seq_len}   device: {device}   "
+          f"decoding: {'greedy' if args.temperature == 0 else f'T={args.temperature}'}")
+
+    def answer(question: str) -> None:
+        ids = fmt.format_for_inference({"messages": [{"role": "user", "content": question}]})
+        x = torch.tensor([ids], dtype=torch.long, device=device)
+        t0 = time.perf_counter()
+        out = model.generate(
+            x, max_new_tokens=args.max_new_tokens, temperature=args.temperature,
+            top_k=args.top_k, top_p=args.top_p,
+            repetition_penalty=args.repetition_penalty, eos_id=tok.eos_id,
+        )
+        dt = time.perf_counter() - t0
+        new = out[0, len(ids):].tolist()
+        stopped = tok.eos_id in new
+        if stopped:
+            new = new[: new.index(tok.eos_id)]
+        print("\n" + "-" * 66)
+        print(tok.decode(new).strip() or "(empty)")
+        print("-" * 66)
+        print(f"{len(new)} tokens in {dt:.2f}s ({len(new)/dt if dt else 0:.1f} tok/s), "
+              f"{'stopped on EOS' if stopped else 'hit token limit'}\n")
+
+    if args.interactive:
+        print("\nInstruct model - ask a question. ('exit' to quit)")
+        while True:
+            try:
+                q = input("\nYou: ").strip()
+            except (EOFError, KeyboardInterrupt):
+                print("\nBye."); break
+            if not q:
+                continue
+            if q.lower() in {"exit", "quit", "q"}:
+                print("Bye."); break
+            answer(q)
+        return 0
+
+    answer(args.prompt)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())