初始化项目，由ModelHub XC社区提供模型

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

mlb/check_sft_format.py

@@ -0,0 +1,38 @@
+import os, json
+HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+os.environ["HF_HOME"] = os.path.join(HERE, "hf_cache")
+import sys; sys.path.insert(0, os.path.join(HERE, "mlb"))
+
+from transformers import AutoTokenizer
+
+MODEL = "Qwen/Qwen3-0.6B"
+DATA = os.path.join(HERE, "data", "sft_tools.jsonl")
+
+def main():
+    tok = AutoTokenizer.from_pretrained(MODEL)
+    rows = [json.loads(l) for l in open(DATA) if l.strip()]
+    print(f"loaded {len(rows)} rows")
+
+    lengths, bad = [], 0
+    for r in rows:
+        text = tok.apply_chat_template(
+            r["messages"], tools=r["tools"], tokenize=False,
+            add_generation_prompt=False, enable_thinking=False)
+        if "<tool_call>" not in text:
+            bad += 1
+        lengths.append(len(tok(text).input_ids))
+
+    lengths.sort()
+    print("tool_call missing in rendered text:", bad)
+    print("token length  min/mean/max:",
+          lengths[0], round(sum(lengths) / len(lengths), 1), lengths[-1])
+    print("p95 length:", lengths[int(0.95 * len(lengths))])
+
+    print("\n----- SAMPLE RENDERED TRAINING STRING -----\n")
+    s = tok.apply_chat_template(rows[0]["messages"], tools=rows[0]["tools"],
+                                tokenize=False, add_generation_prompt=False,
+                                enable_thinking=False)
+    print(s)
+
+if __name__ == "__main__":
+    main()