初始化项目，由ModelHub XC社区提供模型

Model: nttruong1007/qb-granite31-8b
Source: Original Platform

pipeline.py

@@ -0,0 +1,102 @@
+"""Custom Quizbowl pipelines for QANTA 2026 Arena.
+Registers `quizbowl-tossup` and `quizbowl-bonus`. The build script rewrites the
+two flags below per model (LLM vs VLM). Every name passed to register_pipeline
+is imported from transformers, per the submission requirements.
+"""
+import json, re
+from transformers.pipelines import PIPELINE_REGISTRY
+from transformers import (
+    Pipeline,
+    AutoModelForCausalLM, AutoModelForImageTextToText, AutoProcessor,
+)
+
+PT_MODEL = AutoModelForCausalLM   # build script -> AutoModelForImageTextToText for VLMs
+IS_VLM = False                    # build script -> True for VLMs
+
+
+def _to_pil(images):
+    from PIL import Image
+    out = []
+    for im in images or []:
+        out.append(Image.open(im).convert("RGB") if isinstance(im, str) else im)
+    return out
+
+
+def _extract(text):
+    text = re.sub(r"<think>.*?</think>", "", text, flags=re.S)  # strip reasoning traces
+    m = re.search(r"\{.*\}", text, re.S)
+    if m:
+        try:
+            return json.loads(m.group())
+        except Exception:
+            pass
+    return {}
+
+
+class _Base(Pipeline):
+    def _sanitize_parameters(self, **kw):
+        return {}, {}, {}
+
+    def preprocess(self, inputs):
+        return inputs
+
+    def _forward(self, inputs):
+        return {"text": self._gen(self._prompt(inputs), inputs.get("images"))}
+
+    def _proc(self):
+        if not hasattr(self, "_p"):
+            self._p = AutoProcessor.from_pretrained(self.model.name_or_path, trust_remote_code=True)
+        return self._p
+
+    def _gen(self, prompt, images=None):
+        if IS_VLM and images:
+            proc = self._proc()
+            msgs = [{"role": "user", "content": [{"type": "image"} for _ in images] +
+                     [{"type": "text", "text": prompt}]}]
+            chat = proc.apply_chat_template(msgs, add_generation_prompt=True)
+            inp = proc(text=chat, images=_to_pil(images), return_tensors="pt").to(self.model.device)
+            ids = self.model.generate(**inp, max_new_tokens=200, do_sample=False)
+            return proc.batch_decode(ids[:, inp["input_ids"].shape[1]:], skip_special_tokens=True)[0]
+        tok = self.tokenizer
+        if getattr(tok, "chat_template", None):
+            text = tok.apply_chat_template([{"role": "user", "content": prompt}],
+                                           tokenize=False, add_generation_prompt=True)
+        else:
+            text = prompt
+        inp = tok(text, return_tensors="pt").to(self.model.device)
+        ids = self.model.generate(**inp, max_new_tokens=200, do_sample=False,
+                                  pad_token_id=tok.eos_token_id)
+        return tok.decode(ids[0, inp["input_ids"].shape[1]:], skip_special_tokens=True)
+
+
+class QuizbowlTossupPipeline(_Base):
+    def _prompt(self, inputs):
+        return ("You are a quizbowl expert. From the (possibly partial) question, give your best guess.\n"
+                'Respond ONLY as JSON: {"answer": "<concise answer>", "confidence": <0-1 float>, '
+                '"buzz": <true|false>}. Set buzz=true only if confident enough to interrupt.\n'
+                "QUESTION: " + inputs["question_text"])
+
+    def postprocess(self, mo):
+        d = _extract(mo["text"])
+        c = min(max(float(d.get("confidence", 0.5) or 0.5), 0.0), 1.0)
+        return {"answer": str(d.get("answer", "")).strip(),
+                "confidence": c,
+                "buzz": bool(d.get("buzz", c >= 0.7))}
+
+
+class QuizbowlBonusPipeline(_Base):
+    def _prompt(self, inputs):
+        return ("You are a quizbowl expert answering one bonus part.\n"
+                'Respond ONLY as JSON: {"answer": "<concise>", "confidence": <0-1 float>, '
+                '"explanation": "<= 30 words"}.\n'
+                "LEADIN: " + inputs.get("leadin", "") + "\nPART: " + inputs["part"])
+
+    def postprocess(self, mo):
+        d = _extract(mo["text"])
+        c = min(max(float(d.get("confidence", 0.5) or 0.5), 0.0), 1.0)
+        exp = " ".join(str(d.get("explanation", "")).split()[:30])
+        return {"answer": str(d.get("answer", "")).strip(), "confidence": c, "explanation": exp}
+
+
+PIPELINE_REGISTRY.register_pipeline("quizbowl-tossup", pipeline_class=QuizbowlTossupPipeline, pt_model=PT_MODEL)
+PIPELINE_REGISTRY.register_pipeline("quizbowl-bonus",  pipeline_class=QuizbowlBonusPipeline,  pt_model=PT_MODEL)