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Model: T1anyu/DeepInnovator
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- research
+- scientific-discovery
+- idea-generation
+- llm
+- pytorch
+base_model: Qwen/Qwen2.5-14B-Instruct
+---
+
+# DeepInnovator-14B
+
+<p align="center">
+  <a href="https://github.com/HKUDS/DeepInnovator">💻 Code</a> •
+  <a href="https://arxiv.org/abs/2602.18920">📄 Paper</a> •
+  <a href="https://huggingface.co/T1anyu/DeepInnovator">🤗 Model</a>
+</p>
+
+## Model Description
+
+**DeepInnovator** is a Large Language Model trained to possess genuine innovative capability — the ability to autonomously generate novel and significant research ideas. Unlike existing approaches that rely on sophisticated prompt engineering, DeepInnovator is built upon a systematic training paradigm designed to trigger the innovative capability of LLMs.
+
+### Key Features
+
+- 🚀 **Innovative Capability**: Trained specifically for generating novel research ideas
+- 📚 **Knowledge-Grounded**: Leverages structured research knowledge extracted from vast scientific literature
+- 🔄 **Iterative Refinement**: Employs "Next Idea Prediction" paradigm for continuous idea improvement
+- 🏆 **State-of-the-Art Performance**: Achieves 80.53%-93.81% win rates against untrained baselines
+
+## Training Methodology
+
+DeepInnovator comprises two core components:
+
+### 1. "Standing on the Shoulders of Giants"
+An automated data extraction pipeline that extracts and organizes structured research knowledge from a vast corpus of unlabeled scientific literature.
+
+### 2. "Conjectures and Refutations"
+A "Next Idea Prediction" training paradigm that models the generation of research ideas as an iterative process of continuously predicting, evaluating, and refining plausible and novel next ideas.
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "T1anyu/DeepInnovator"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+prompt = "Based on the recent advances in graph neural networks and large language models, propose a novel research idea:"
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+)
+
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Using vLLM for Faster Inference
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="T1anyu/DeepInnovator")
+sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=1024)
+
+prompt = "Based on the recent advances in graph neural networks and large language models, propose a novel research idea:"
+outputs = llm.generate([prompt], sampling_params)
+
+print(outputs[0].outputs[0].text)
+```
+
+## Evaluation Results
+
+Both automatic and expert evaluations demonstrate that DeepInnovator-14B significantly outperforms untrained baselines:
+
+| Comparison | Win Rate |
+|------------|----------|
+| vs. Untrained Baselines | 80.53% - 93.81% |
+| vs. Leading LLMs | Comparable Performance |
+
+## Citation
+
+If you find DeepInnovator useful in your research, please cite our paper:
+
+```bibtex
+@article{fan2026deepinnovator,
+  title={DeepInnovator: Triggering the Innovative Capabilities of LLMs},
+  author={Fan, Tianyu and Zhang, Fengji and Zheng, Yuxiang and Chen, Bei and Niu, Xinyao and Huang, Chengen and Lin, Junyang and Huang, Chao},
+  journal={arXiv preprint arXiv:2602.18920},
+  year={2026}
+}
+```
+
+## License
+
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Links
+
+- **GitHub Repository**: [https://github.com/HKUDS/DeepInnovator](https://github.com/HKUDS/DeepInnovator)
+- **Hugging Face Model**: [https://huggingface.co/T1anyu/DeepInnovator](https://huggingface.co/T1anyu/DeepInnovator)
+
+## Acknowledgements
+
+This work is developed by the [HKU Data Science Lab (HKUDS)](https://github.com/HKUDS).

added_tokens.json

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+  "vocab_size": 152064
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configuration.json

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generation_config.json

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