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--- |
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license: apache-2.0 |
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datasets: |
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- 6cf/liveideabench |
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language: |
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- en |
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base_model: |
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- Qwen/QwQ-32B-Preview |
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tags: |
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- chemistry |
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- biology |
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- climate |
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- medical |
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--- |
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# IdeaWhiz Model Card 🧠 |
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## Model Summary 🔬 |
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IdeaWhiz is a fine-tuned version of QwQ-32B-Preview, specifically optimized for scientific creativity and step-by-step reasoning. The model leverages the LiveIdeaBench dataset to enhance its capabilities in generating novel scientific ideas and hypotheses. |
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## Key Features 🌟 |
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- Base Model: QwQ-32B-Preview 🔋 |
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- Training Dataset: LiveIdeaBench 📊 |
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- Main Focus: Scientific creativity and idea generation 💡 |
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- Reasoning Style: o1-style step-by-step reasoning ⚡ |
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## Intended Use 🎯 |
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- Scientific hypothesis generation 🧪 |
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- Creative problem-solving in research 🔍 |
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- Step-by-step scientific reasoning 📝 |
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- Research direction brainstorming 🌱 |
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## Model Performance Compared to QwQ-32B-Preview |
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## Quickstart 🚀 |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "6cf/QwQ-32B-Preview-IdeaWhiz-v1" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = """I'll be submitting your next responses to a "Good Scientific Idea" expert review panel. If they consider your idea to be a good one, you'll receive a reward. Your assigned keyword is: "cancer". You may provide background information. The idea MUST be within 100 words (including background information). (Note: good scientific ideas should be novel, verifiable, practically valuable, and able to advance the field.). NOTE: You MUST give your answer after **Final Idea:** |
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""" |
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messages = [ |
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{"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=4096 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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# Example Outputs |
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``` |
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Alright, so I have this task to come up with a good scientific idea related to cancer, |
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and it has to be novel, verifiable, practically valuable, and able to advance the field. |
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Plus, it needs to be within 100 words, including any background information. |
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That's a pretty tight constraint, but I think I can manage. First, ... |
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... |
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**Final Idea:** propose a novel approach to cancer treatment by developing personalized |
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cancer vaccines tailored to each patient's unique tumor mutations. By integrating machine |
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learning algorithms with comprehensive omics data, including genomics, epigenetics, |
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transcriptomics, and proteomics, this method aims to identify and prioritize the most |
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immunogenic tumor-specific antigens. This integrated strategy could enhance vaccine |
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efficacy and represents an advancement in precision medicine for cancer treatment. |
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``` |
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# Traning Dataset |
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## 🤖💡 LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context |
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### Dataset |
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[](https://huggingface.co/datasets/6cf/liveideabench) |
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### Paper |
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[](https://arxiv.org/abs/2412.17596) |
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If you use this model, please cite: |
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``` |
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@article{ruan2024liveideabench, |
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title={LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context}, |
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author={Ruan, Kai and Wang, Xuan and Hong, Jixiang and Sun, Hao}, |
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journal={arXiv preprint arXiv:2412.17596}, |
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year={2024} |
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} |
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``` |
