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metadata

language:
  - zh
  - en
license: apache-2.0
tags:
  - biomedical
  - research-assistant
  - qwen2
  - chinese
  - intervention
  - medical
  - proactive-agent
library_name: transformers
pipeline_tag: text-generation

CoLabScience: Proactive Research Assistant for Biomedical Interventions

An intelligent proactive assistant specialized in biomedical research and intervention studies

📖 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

🏗️ 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

🚀 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)

💡 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

📊 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.

⚠️ 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

📈 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.

🛠️ 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
}

🤝 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

📄 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.

🔗 Related Resources

Models

Datasets

Tools

📞 Contact

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

🙏 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

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

Made with ❤️ for biomedical research

🤗 Model Hub • 📖 Documentation • 💬 Discussions