Update README.md

README.md

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 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6205fefd3f1dc8a642d70b10/JEZgA_xV6oF8AIsya9dop.jpeg)
 
 
-# Model Card for Model ID
+# IdeaWhiz Model Card 🧠
 
-Comming Soon
+## 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 ⚡
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Intended Use 🎯
+- Scientific hypothesis generation 🧪
+- Creative problem-solving in research 🔍
+- Step-by-step scientific reasoning 📝
+- Research direction brainstorming 🌱
 
+## Quickstart 🚀
 
-## Model Details
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+model_name = "6cf/QwQ-32B-Preview-IdeaWhiz-v1"
 
-## Training Details
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-### Training Data
+
+
+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. -->
 
 
-# 🤖💡 LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
+# Traning Dataset
+
+## 🤖💡 LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
 
 
 ### Dataset