初始化项目，由ModelHub XC社区提供模型

Model: T1anyu/DeepInnovator
Source: Original Platform

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).