---
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

<div align="center">

[![Model](https://img.shields.io/badge/Model-Qwen2--1.5B-blue)](https://huggingface.co/YangWu001/intervention_chinese)
[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
[![Language](https://img.shields.io/badge/Language-Chinese%20%7C%20English-orange)](https://huggingface.co/YangWu001/intervention_chinese)

*An intelligent proactive assistant specialized in biomedical research and intervention studies*

</div>

---

## 📖 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

```bash
pip install transformers torch
```

### Quick Start

```python
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

```python
# 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

```json
{
  "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

```python
# 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](https://www.apache.org/licenses/LICENSE-2.0) for full terms.

---

## 🔗 Related Resources

### Models
- [Qwen2 Base Models](https://huggingface.co/Qwen)
- [BioGPT](https://huggingface.co/microsoft/biogpt)
- [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract)

### Datasets
- [PubMed](https://pubmed.ncbi.nlm.nih.gov/)
- [ClinicalTrials.gov](https://clinicaltrials.gov/)
- [MIMIC-III](https://physionet.org/content/mimiciii/)

### Tools
- [Transformers Library](https://github.com/huggingface/transformers)
- [PyTorch](https://pytorch.org/)
- [Hugging Face Hub](https://huggingface.co/)

---

## 📞 Contact

- **Model Author**: Yang Wu
- **HuggingFace Profile**: [@YangWu001](https://huggingface.co/YangWu001)
- **Issues**: [Report on HuggingFace](https://huggingface.co/YangWu001/intervention_chinese/discussions)

---

## 🙏 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

---

<div align="center">

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

Made with ❤️ for biomedical research

[🤗 Model Hub](https://huggingface.co/YangWu001/intervention_chinese) • [📖 Documentation](https://huggingface.co/YangWu001/intervention_chinese) • [💬 Discussions](https://huggingface.co/YangWu001/intervention_chinese/discussions)

</div>

