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NOTICE

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+Copyright 2023-2024 01.AI
+
+This work, [Your Model Name], is based on the work originally authored by 01.AI.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Attribution 
+
+If you create derivative works based on this model, please include the following attribution in your derivative works:
+
+This work is a derivative of [The Yi-1.5 Series Model You Base On] by 01.AI, used under the Apache 2.0 License.
+
+
+

README.md

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+---
+license: apache-2.0
+---
+<div align="center">
+
+<picture> 
+  <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
+</picture>
+
+</div>
+
+<p align="center">
+  <a href="https://github.com/01-ai">🐙 GitHub</a> •
+  <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
+  <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
+  <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> 
+  <br/>
+  <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
+  <a href="https://01-ai.github.io/">💪 Tech Blog</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
+</p>
+
+# Intro
+
+Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. 
+
+Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
+
+<div align="center">
+  
+Model | Context Length | Pre-trained Tokens
+| :------------: | :------------: | :------------: |
+| Yi-1.5 | 4K, 16K, 32K | 3.6T
+
+</div>
+
+# Models
+
+- Chat models
+
+  <div align="center">
+  
+  | Name            | Download                                                                                                                                                            |
+  | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+  | Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI)|
+  | Yi-1.5-34B-Chat-16K  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-9B-Chat  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-9B-Chat-16K  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-6B-Chat  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  
+  </div>
+
+- Base models
+
+  <div align="center">
+  
+  | Name       | Download                                                                                                                                                            |
+  | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+  | Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-9B-32K  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  | Yi-1.5-6B  | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
+  
+  </div>
+
+# Benchmarks
+
+- Chat models
+
+  Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
+
+  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png)
+
+  Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
+
+  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png)
+
+- Base models
+
+  Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
+
+  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png)
+
+  Yi-1.5-9B is the top performer among similarly sized open-source models.
+
+  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png)
+
+# Quick Start
+
+For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
+

config.json

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+  "use_cache": false,
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+      ]
+    },
+    "quizbowl-bonus": {
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+      "pt": [
+        "AutoModelForCausalLM"
+      ]
+    }
+  }
+}

generation_config.json

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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.40.0"
+}

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+}

pipeline.py

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+"""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)

requirements.txt

@@ -0,0 +1,8 @@
+transformers>=4.49.0
+torch
+accelerate
+pillow
+sentencepiece
+einops
+timm
+qwen-vl-utils

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
+size 1033105

tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 4096,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "split_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}