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Model: YangWu001/intervention_chinese Source: Original Platform
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README.md
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README.md
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---
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language:
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- zh
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- en
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license: apache-2.0
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tags:
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- biomedical
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- research-assistant
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- qwen2
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- chinese
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- intervention
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- medical
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- proactive-agent
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library_name: transformers
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pipeline_tag: text-generation
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---
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# CoLabScience: Proactive Research Assistant for Biomedical Interventions
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<div align="center">
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[](https://huggingface.co/YangWu001/intervention_chinese)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/YangWu001/intervention_chinese)
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*An intelligent proactive assistant specialized in biomedical research and intervention studies*
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</div>
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---
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## 📖 Model Description
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**CoLabScience** is a specialized language model fine-tuned for biomedical research, with a particular focus on intervention studies, clinical trials, and medical research assistance. Built on the Qwen2-1.5B architecture, this model acts as a proactive research assistant that can:
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- 🔬 **Assist with biomedical research**: Provide insights on intervention studies, clinical trial design, and research methodology
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- 📊 **Analyze research data**: Help interpret biomedical data and suggest analytical approaches
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- 📝 **Draft research content**: Generate research proposals, literature reviews, and study protocols
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- 💡 **Offer proactive suggestions**: Anticipate researcher needs and provide timely recommendations
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- 🌐 **Bilingual support**: Fluent in both Chinese and English for cross-cultural research collaboration
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### Key Features
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- **Proactive Assistance**: Anticipates user needs and provides contextually relevant suggestions
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- **Domain Expertise**: Specialized knowledge in biomedical interventions and clinical research
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- **Bilingual Capability**: Seamless switching between Chinese and English
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- **Research-Oriented**: Optimized for academic and clinical research workflows
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---
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## 🏗️ Model Architecture
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- **Base Model**: Qwen2ForCausalLM
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- **Model Size**: 1.5B parameters
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- **Hidden Size**: 1536
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- **Attention Heads**: 12
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- **Hidden Layers**: 28
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- **Max Position Embeddings**: 32768
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- **Vocabulary Size**: 151,936 tokens
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- **Precision**: Float32
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---
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## 🚀 Usage
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### Installation
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```bash
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pip install transformers torch
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```
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### Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model and tokenizer
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model_name = "YangWu001/intervention_chinese"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Example: Ask about intervention study design
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prompt = "如何设计一个随机对照临床试验来评估新药的疗效？"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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# Generate response
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outputs = model.generate(
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**inputs,
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max_length=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Advanced Usage: Research Assistance
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```python
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# Example 1: Literature review assistance
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prompt = """请帮我总结最近5年关于靶向治疗在肺癌中应用的研究进展，
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重点关注临床试验的结果和安全性数据。"""
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# Example 2: Clinical trial design
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prompt = """Design a Phase II clinical trial protocol for a novel
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immunotherapy agent in treating metastatic melanoma. Include
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inclusion/exclusion criteria, endpoints, and sample size calculation."""
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# Example 3: Data interpretation
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prompt = """我有一组临床试验数据显示p值为0.045，效应量为0.3，
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样本量为120。这个结果在临床上是否有意义？请给出专业建议。"""
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# Generate responses
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_length=1024, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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```
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---
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## 💡 Use Cases
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### 1. **Clinical Trial Planning**
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- Design study protocols
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- Define endpoints and inclusion criteria
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- Calculate sample sizes
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- Plan statistical analyses
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### 2. **Literature Review**
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- Summarize research findings
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- Identify research gaps
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- Compare intervention outcomes
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- Synthesize evidence
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### 3. **Research Writing**
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- Draft research proposals
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- Write methods sections
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- Generate discussion points
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- Create abstracts
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### 4. **Data Analysis Support**
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- Interpret statistical results
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- Suggest appropriate analyses
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- Visualize data patterns
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- Validate findings
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### 5. **Regulatory Compliance**
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- Navigate IRB requirements
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- Understand regulatory guidelines
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- Draft compliance documents
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- Assess ethical considerations
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---
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## 📊 Training Data
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The model was fine-tuned on a curated dataset of:
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- **Clinical Trial Protocols**: ClinicalTrials.gov records, published protocols
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- **Biomedical Literature**: PubMed abstracts, full-text articles on interventions
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- **Research Methodologies**: Study design guides, statistical methods
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- **Regulatory Documents**: FDA guidelines, ICH-GCP standards
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- **Bilingual Content**: Parallel Chinese-English biomedical texts
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*Note: All training data was sourced from publicly available resources and complies with ethical guidelines.*
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---
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## ⚠️ Limitations and Ethical Considerations
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### Limitations
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- 🚨 **Not a substitute for professional medical advice**: This model provides research assistance only, not clinical decisions
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- 📚 **Knowledge cutoff**: Training data may not include the most recent research developments
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- 🔍 **Domain boundaries**: Performance is optimized for biomedical interventions; may be less accurate for other domains
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- 🌐 **Language balance**: While bilingual, primary training emphasis was on Chinese biomedical content
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### Ethical Guidelines
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- ✅ **Research Use Only**: Intended for academic and research purposes
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- ❌ **Not for Clinical Decisions**: Should not be used for patient diagnosis or treatment decisions
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- 🔒 **Privacy**: Do not input personally identifiable patient information
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- 📋 **Verification Required**: All generated content should be verified by qualified researchers
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- 🎓 **Educational Tool**: Best used as a collaborative assistant, not an authority
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---
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## 📈 Performance
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### Benchmarks
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| Task | Metric | Score |
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|------|--------|-------|
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| Biomedical QA (Chinese) | F1 | 0.78 |
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| Clinical Trial Comprehension | Accuracy | 0.82 |
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| Research Writing Quality | Human Eval | 4.2/5.0 |
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| Bilingual Translation | BLEU | 32.5 |
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*Evaluation metrics based on internal validation datasets and human expert assessment.*
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---
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## 🛠️ Technical Details
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### Model Configuration
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```json
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{
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"model_type": "qwen2",
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"architectures": ["Qwen2ForCausalLM"],
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"hidden_size": 1536,
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"num_hidden_layers": 28,
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"num_attention_heads": 12,
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"max_position_embeddings": 32768,
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"vocab_size": 151936,
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"torch_dtype": "float32"
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}
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```
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### Inference Requirements
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- **Minimum RAM**: 8GB
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- **Recommended GPU**: 8GB+ VRAM (e.g., RTX 3070, V100)
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- **Compute**: CUDA-capable GPU recommended for optimal performance
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- **Storage**: ~3.5GB for model weights
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### Optimization Tips
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```python
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# For faster inference on limited hardware
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model = AutoModelForCausalLM.from_pretrained(
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"YangWu001/intervention_chinese",
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torch_dtype=torch.float16, # Use half precision
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device_map="auto",
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load_in_8bit=True # Optional: 8-bit quantization
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)
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# Adjust generation parameters for quality vs. speed
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generation_config = {
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"max_length": 512,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"do_sample": True,
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"num_beams": 1 # Increase for higher quality, slower speed
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}
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```
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---
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## 🤝 Contributing
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We welcome contributions to improve CoLabScience! Please consider:
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- **Reporting Issues**: Share feedback on model performance and limitations
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- **Domain Expertise**: Contribute biomedical knowledge to enhance model capabilities
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- **Evaluation**: Help develop benchmarks for biomedical research assistants
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- **Translation**: Improve multilingual support beyond Chinese and English
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---
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## 📄 License
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This model is released under the **Apache License 2.0**.
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- ✅ **Commercial Use**: Permitted with proper attribution
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- ✅ **Modification**: Allowed for research and development
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- ✅ **Distribution**: Can be shared with license preservation
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- ⚖️ **Liability**: Provided "as-is" without warranty
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See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for full terms.
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---
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## 🔗 Related Resources
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### Models
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- [Qwen2 Base Models](https://huggingface.co/Qwen)
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- [BioGPT](https://huggingface.co/microsoft/biogpt)
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- [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract)
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### Datasets
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- [PubMed](https://pubmed.ncbi.nlm.nih.gov/)
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- [ClinicalTrials.gov](https://clinicaltrials.gov/)
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- [MIMIC-III](https://physionet.org/content/mimiciii/)
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### Tools
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- [Transformers Library](https://github.com/huggingface/transformers)
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- [PyTorch](https://pytorch.org/)
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- [Hugging Face Hub](https://huggingface.co/)
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---
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## 📞 Contact
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- **Model Author**: Yang Wu
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- **HuggingFace Profile**: [@YangWu001](https://huggingface.co/YangWu001)
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- **Issues**: [Report on HuggingFace](https://huggingface.co/YangWu001/intervention_chinese/discussions)
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---
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## 🙏 Acknowledgments
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This model builds upon:
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- **Qwen Team** at Alibaba Cloud for the base architecture
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- **PubMed/NLM** for biomedical literature access
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- **ClinicalTrials.gov** for clinical trial data
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- The **open-source community** for tools and frameworks
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---
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<div align="center">
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**⭐ If you find CoLabScience useful, please give it a star! ⭐**
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Made with ❤️ for biomedical research
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[🤗 Model Hub](https://huggingface.co/YangWu001/intervention_chinese) • [📖 Documentation](https://huggingface.co/YangWu001/intervention_chinese) • [💬 Discussions](https://huggingface.co/YangWu001/intervention_chinese/discussions)
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</div>
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