YangWu001/intervention_chinese

CoLabScience: Proactive Research Assistant for Biomedical Interventions

An intelligent proactive assistant specialized in biomedical research and intervention studies

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📖 Model Description

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:

• 🔬 Assist with biomedical research: Provide insights on intervention studies, clinical trial design, and research methodology
• 📊 Analyze research data: Help interpret biomedical data and suggest analytical approaches
• 📝 Draft research content: Generate research proposals, literature reviews, and study protocols
• 💡 Offer proactive suggestions: Anticipate researcher needs and provide timely recommendations
• 🌐 Bilingual support: Fluent in both Chinese and English for cross-cultural research collaboration

Key Features

• Proactive Assistance: Anticipates user needs and provides contextually relevant suggestions
• Domain Expertise: Specialized knowledge in biomedical interventions and clinical research
• Bilingual Capability: Seamless switching between Chinese and English
• Research-Oriented: Optimized for academic and clinical research workflows

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🏗️ Model Architecture

• Base Model: Qwen2ForCausalLM
• Model Size: 1.5B parameters
• Hidden Size: 1536
• Attention Heads: 12
• Hidden Layers: 28
• Max Position Embeddings: 32768
• Vocabulary Size: 151,936 tokens
• Precision: Float32

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🚀 Usage

Installation

pip install transformers torch

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "YangWu001/intervention_chinese"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example: Ask about intervention study design
prompt = "如何设计一个随机对照临床试验来评估新药的疗效？"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    **inputs,
    max_length=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Advanced Usage: Research Assistance

# Example 1: Literature review assistance
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，
重点关注临床试验的结果和安全性数据。"""

# Example 2: Clinical trial design
prompt = """Design a Phase II clinical trial protocol for a novel 
immunotherapy agent in treating metastatic melanoma. Include 
inclusion/exclusion criteria, endpoints, and sample size calculation."""

# Example 3: Data interpretation
prompt = """我有一组临床试验数据显示p值为0.045，效应量为0.3，
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。"""

# Generate responses
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=1024, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

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💡 Use Cases

1. Clinical Trial Planning

• Design study protocols
• Define endpoints and inclusion criteria
• Calculate sample sizes
• Plan statistical analyses

2. Literature Review

• Summarize research findings
• Identify research gaps
• Compare intervention outcomes
• Synthesize evidence

3. Research Writing

• Draft research proposals
• Write methods sections
• Generate discussion points
• Create abstracts

4. Data Analysis Support

• Interpret statistical results
• Suggest appropriate analyses
• Visualize data patterns
• Validate findings

5. Regulatory Compliance

• Navigate IRB requirements
• Understand regulatory guidelines
• Draft compliance documents
• Assess ethical considerations

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📊 Training Data

The model was fine-tuned on a curated dataset of:

• Clinical Trial Protocols: ClinicalTrials.gov records, published protocols
• Biomedical Literature: PubMed abstracts, full-text articles on interventions
• Research Methodologies: Study design guides, statistical methods
• Regulatory Documents: FDA guidelines, ICH-GCP standards
• Bilingual Content: Parallel Chinese-English biomedical texts

Note: All training data was sourced from publicly available resources and complies with ethical guidelines.

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⚠️ Limitations and Ethical Considerations

Limitations

• 🚨 Not a substitute for professional medical advice: This model provides research assistance only, not clinical decisions
• 📚 Knowledge cutoff: Training data may not include the most recent research developments
• 🔍 Domain boundaries: Performance is optimized for biomedical interventions; may be less accurate for other domains
• 🌐 Language balance: While bilingual, primary training emphasis was on Chinese biomedical content

Ethical Guidelines

• ✅ Research Use Only: Intended for academic and research purposes
• ❌ Not for Clinical Decisions: Should not be used for patient diagnosis or treatment decisions
• 🔒 Privacy: Do not input personally identifiable patient information
• 📋 Verification Required: All generated content should be verified by qualified researchers
• 🎓 Educational Tool: Best used as a collaborative assistant, not an authority

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📈 Performance

Benchmarks

Task	Metric	Score
Biomedical QA (Chinese)	F1	0.78
Clinical Trial Comprehension	Accuracy	0.82
Research Writing Quality	Human Eval	4.2/5.0
Bilingual Translation	BLEU	32.5

Evaluation metrics based on internal validation datasets and human expert assessment.

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🛠️ Technical Details

Model Configuration

{
  "model_type": "qwen2",
  "architectures": ["Qwen2ForCausalLM"],
  "hidden_size": 1536,
  "num_hidden_layers": 28,
  "num_attention_heads": 12,
  "max_position_embeddings": 32768,
  "vocab_size": 151936,
  "torch_dtype": "float32"
}

Inference Requirements

• Minimum RAM: 8GB
• Recommended GPU: 8GB+ VRAM (e.g., RTX 3070, V100)
• Compute: CUDA-capable GPU recommended for optimal performance
• Storage: ~3.5GB for model weights

Optimization Tips

# For faster inference on limited hardware
model = AutoModelForCausalLM.from_pretrained(
    "YangWu001/intervention_chinese",
    torch_dtype=torch.float16,  # Use half precision
    device_map="auto",
    load_in_8bit=True  # Optional: 8-bit quantization
)

# Adjust generation parameters for quality vs. speed
generation_config = {
    "max_length": 512,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "repetition_penalty": 1.1,
    "do_sample": True,
    "num_beams": 1  # Increase for higher quality, slower speed
}

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🤝 Contributing

We welcome contributions to improve CoLabScience! Please consider:

• Reporting Issues: Share feedback on model performance and limitations
• Domain Expertise: Contribute biomedical knowledge to enhance model capabilities
• Evaluation: Help develop benchmarks for biomedical research assistants
• Translation: Improve multilingual support beyond Chinese and English

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📄 License

This model is released under the Apache License 2.0.

• ✅ Commercial Use: Permitted with proper attribution
• ✅ Modification: Allowed for research and development
• ✅ Distribution: Can be shared with license preservation
• ⚖️ Liability: Provided "as-is" without warranty

See LICENSE for full terms.

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🔗 Related Resources

Models

• Qwen2 Base Models
• BioGPT
• PubMedBERT

Datasets

• PubMed
• ClinicalTrials.gov
• MIMIC-III

Tools

• Transformers Library
• PyTorch
• Hugging Face Hub

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📞 Contact

• Model Author: Yang Wu
• HuggingFace Profile: @YangWu001
• Issues: Report on HuggingFace

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🙏 Acknowledgments

This model builds upon:

• Qwen Team at Alibaba Cloud for the base architecture
• PubMed/NLM for biomedical literature access
• ClinicalTrials.gov for clinical trial data
• The open-source community for tools and frameworks

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⭐ If you find CoLabScience useful, please give it a star! ⭐

Made with ❤️ for biomedical research

🤗 Model Hub • 📖 Documentation • 💬 Discussions