初始化项目，由ModelHub XC社区提供模型

Model: karthik-2905/AL1-model-B
Source: Original Platform

mlb/eval_tools.py

@@ -0,0 +1,58 @@
+import os, json, argparse
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+os.environ["HF_HOME"] = os.path.join(HERE, "hf_cache")
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from tools_common import TOOLS, parse_tool_calls
+
+def norm_args(d):
+    return {k: (v.strip() if isinstance(v, str) else v) for k, v in (d or {}).items()}
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", default="Qwen/Qwen3-0.6B")
+    ap.add_argument("--adapter", default=None)
+    ap.add_argument("--eval", default=os.path.join(HERE, "eval", "tools_eval.py"))
+    ap.add_argument("--out", default=os.path.join(HERE, "eval", "baseline_tools.json"))
+    a = ap.parse_args()
+
+    device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
+    tok = AutoTokenizer.from_pretrained(a.model)
+    model = AutoModelForCausalLM.from_pretrained(a.model, dtype=torch.float32).to(device)
+    if a.adapter:
+        from peft import PeftModel
+        model = PeftModel.from_pretrained(model, a.adapter).to(device)
+    model.eval()
+
+    cases = [json.loads(l) for l in open(a.eval) if l.strip()]
+    results, name_hits, full_hits = [], 0, 0
+
+    for c in cases:
+        msgs = [{"role": "user", "content": c["query"]}]
+        text = tok.apply_chat_template(msgs, tools=TOOLS, tokenize=False,
+                                       add_generation_prompt=True, enable_thinking=False)
+        ids = tok(text, return_tensors="pt").to(device)
+        out = model.generate(**ids, max_new_tokens=128, do_sample=False)
+        reply = tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True)
+
+        calls = parse_tool_calls(reply)
+        got = calls[0] if calls else None
+        exp = c["expected"]
+        name_ok = bool(got) and got.get("name") == exp["name"]
+        full_ok = name_ok and norm_args(got.get("arguments")) == norm_args(exp["arguments"])
+        name_hits += name_ok
+        full_hits += full_ok
+        results.append({"query": c["query"], "expected": exp,
+                        "got": got, "raw": reply,
+                        "name_ok": name_ok, "full_ok": full_ok})
+
+    n = len(cases)
+    summary = {"model": a.model, "adapter": a.adapter, "n": n,
+               "name_acc": round(name_hits / n, 3),
+               "full_acc": round(full_hits / n, 3)}
+    json.dump({"summary": summary, "results": results}, open(a.out, "w"), indent=2)
+    print(json.dumps(summary, indent=2))
+
+if __name__ == "__main__":
+    main()