初始化项目，由ModelHub XC社区提供模型

Model: dipta007/decomposeRL-7b
Source: Original Platform

example.py

@@ -0,0 +1,138 @@
+"""Run DecomposeRL-7B on a (claim, evidence_doc) pair and pretty-print the trace.
+
+Usage:
+    python example.py
+"""
+
+import re
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_NAME = "dipta007/decomposeRL-7b"
+
+PROMPT_TEMPLATE = """You are tasked with systematically verifying the accuracy of a claim. You will be provided with a claim to verify and an evidence document to consult.
+
+Here is the evidence document you should consult:
+
+<evidence_document>
+{evidence_doc}
+</evidence_document>
+
+Here is the claim you need to verify:
+
+<claim>
+{claim}
+</claim>
+
+Your task is to verify whether this claim is Supported or Refuted through an iterative process of asking questions and gathering information.
+
+# Verification Process
+
+Begin by analyzing the claim in <think> tags, then enter an iterative cycle of <question>/<answer> pairs answered ONLY from the evidence document. When every sub-claim is addressed, output your final label inside <verification> tags. The label must be exactly one of: Supported, Refuted.
+
+Stop immediately after the closing </verification> tag.
+
+Begin your verification process now."""
+
+TAG_RE = re.compile(r"<(think|question|answer|verification)>(.*?)</\1>", re.DOTALL)
+
+
+def build_prompt(claim: str, evidence_doc: str) -> str:
+    """Wrap a claim and evidence document in the DecomposeRL verification prompt."""
+    return PROMPT_TEMPLATE.format(claim=claim, evidence_doc=evidence_doc)
+
+
+def verify(
+    model,
+    tokenizer,
+    claim: str,
+    evidence_doc: str,
+    max_new_tokens: int = 4500,
+    temperature: float = 0.7,
+) -> str:
+    """Run the model end-to-end on a (claim, evidence_doc) pair and return the raw trace."""
+    messages = [{"role": "user", "content": build_prompt(claim, evidence_doc)}]
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    out = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        do_sample=True,
+    )
+    return tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+
+def parse_trace(text: str):
+    """Return a list of (tag, content) tuples in the order they appear."""
+    return [(tag, body.strip()) for tag, body in TAG_RE.findall(text)]
+
+
+def pretty_print(text: str) -> None:
+    """Print the trace as a readable conversation. Falls back to raw output if degenerate."""
+    parsed = parse_trace(text)
+    tags = {tag for tag, _ in parsed}
+    if not parsed or "verification" not in tags:
+        print("⚠️  Could not parse output into the expected think/question/answer/verification structure.")
+        print("Raw output:")
+        print("─" * 78)
+        print(text)
+        print("─" * 78)
+        return
+
+    cycle_idx = 0
+    pending_q = None
+    for tag, body in parsed:
+        if tag == "think":
+            print("─" * 78)
+            print("🧠  THINK")
+            print("─" * 78)
+            print(body)
+            print()
+        elif tag == "question":
+            cycle_idx += 1
+            pending_q = body
+        elif tag == "answer":
+            print(f"🔸  Q{cycle_idx}: {pending_q}")
+            print(f"💬  A{cycle_idx}: {body}")
+            print()
+            pending_q = None
+        elif tag == "verification":
+            print("=" * 78)
+            print(f"✅  VERIFICATION: {body}")
+            print("=" * 78)
+
+
+def extract_label(text: str):
+    """Return 'Supported', 'Refuted', or None."""
+    match = re.search(r"<verification>\s*(Supported|Refuted)\s*</verification>", text)
+    return match.group(1) if match else None
+
+
+def main():
+    print(f"Loading {MODEL_NAME} ...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        torch_dtype="auto",
+        device_map="auto",
+    )
+
+    evidence_doc = (
+        "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, "
+        "France. It is named after the engineer Gustave Eiffel, whose company designed and "
+        "built the tower from 1887 to 1889. Locally nicknamed 'La dame de fer', it was "
+        "constructed as the centerpiece of the 1889 World's Fair. The tower is 330 metres "
+        "(1,083 ft) tall."
+    )
+    claim = "The Eiffel Tower was completed in 1887 and stands 330 metres tall."
+
+    print(f"\nClaim: {claim}\n")
+    response = verify(model, tokenizer, claim, evidence_doc)
+
+    pretty_print(response)
+    print(f"\nFinal label: {extract_label(response)}")
+
+
+if __name__ == "__main__":
+    main()