Update README.md

README.md

@@ -14,3 +14,106 @@ tags:
 - medical
 ---
 
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6205fefd3f1dc8a642d70b10/JEZgA_xV6oF8AIsya9dop.jpeg)
+
+
+# IdeaWhiz Model Card 🧠
+
+## Model Summary 🔬
+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.
+
+## Key Features 🌟
+- Base Model: QwQ-32B-Preview 🔋
+- Training Dataset: LiveIdeaBench 📊
+- Main Focus: Scientific creativity and idea generation 💡
+- Reasoning Style: o1-style step-by-step reasoning ⚡
+
+## Intended Use 🎯
+- Scientific hypothesis generation 🧪
+- Creative problem-solving in research 🔍
+- Step-by-step scientific reasoning 📝
+- Research direction brainstorming 🌱
+
+## Quickstart 🚀
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "6cf/QwQ-32B-Preview-IdeaWhiz-v1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+
+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:**
+"""
+
+
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=4096
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+<!-- 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. -->
+
+# Example Outputs
+
+```
+Alright, 
+
+...
+
+**Final Idea:** Engineering exosomes to express checkpoint inhibitors on their surface for targeted delivery to the tumor microenvironment, with the aim of enhancing
+ immune-mediated elimination of cancer cells. This approach combines the natural tropism of exosomes for tumor sites with the immunostimulatory effects of checkpoint inhibitors, potentially leading to more effective and less toxic cancer treatments.
+```
+
+
+
+# Traning Dataset
+
+## 🤖💡 LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
+
+
+### Dataset
+
+[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/6cf/liveideabench)
+
+### Paper
+
+[![arXiv](https://img.shields.io/badge/arXiv-2412.17596-b31b1b.svg)](https://arxiv.org/abs/2412.17596)
+
+
+
+If you use this model, please cite:
+
+```
+@article{ruan2024liveideabench,
+title={LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context},
+author={Ruan, Kai and Wang, Xuan and Hong, Jixiang and Sun, Hao},
+journal={arXiv preprint arXiv:2412.17596},
+year={2024}
+}
+```