YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

CoLabScience-Generator-EN: Intervention Content Generation Model (English)

Professional biomedical intervention content generation model - English Edition

📖 Model Description

CoLabScience-Generator-EN is a specialized large language model for generating biomedical intervention research content, built on the Gemma3-12B-IT architecture and fine-tuned on curated English biomedical data. This model focuses on:

🔬 Intervention Research Content Generation: Generate clinical trial protocols, study designs, intervention descriptions
📊 Data Analysis Recommendations: Provide statistical analysis methods and data interpretation suggestions
📝 Research Document Writing: Assist in writing research proposals, literature reviews, research reports
💡 Proactive Research Assistance: Anticipate researcher needs and provide timely professional suggestions
🌍 English Optimization: Optimized specifically for English biomedical research scenarios

Key Features

Domain Expertise: Deep focus on biomedical intervention research and clinical trials
Large-Scale Parameters: 12B parameter scale for enhanced reasoning and generation capabilities
English Native Support: Trained on English data for natural and fluent English expression
Research-Oriented: Optimized for academic and clinical research workflows
High-Quality Output: Generate professional, accurate, academically compliant content

🏗️ Model Architecture

Base Model: Gemma3ForCausalLM (12B)
Model Size: ~12B parameters
Hidden Size: 4096
Attention Heads: 16 (with 8 key-value heads)
Hidden Layers: 42
Head Dimension: 256
Max Position Embeddings: 32768
Vocabulary Size: 262,144 tokens
Precision: BFloat16
Fine-tuning Method: LoRA + Full Model Merge

🚀 Usage

Installation

pip install transformers torch vllm

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "YangWu001/intervention_english_generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Generate clinical trial design content
prompt = """Design a randomized controlled trial to evaluate the efficacy 
of a novel targeted drug in patients with advanced non-small cell lung cancer. 
Please include:
1. Study objectives and hypotheses
2. Inclusion and exclusion criteria
3. Primary and secondary endpoints
4. Sample size calculation
5. Statistical analysis plan"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

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

Using vLLM for Efficient Inference

from vllm import LLM, SamplingParams

# Initialize model
llm = LLM(
    model="YangWu001/intervention_english_generator",
    tensor_parallel_size=2,  # Use 2 GPUs
    dtype="bfloat16",
    gpu_memory_utilization=0.85,
    max_model_len=8192
)

# Set sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048
)

# Batch generation
prompts = [
    "Describe a clinical trial protocol for evaluating immunotherapy",
    "How to design a dose-escalation study?",
    "Explain intention-to-treat analysis (ITT)"
]

outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(f"Generated: {output.outputs[0].text}\n")

Advanced Usage: Research Content Generation

# Example 1: Clinical trial protocol generation
prompt = """Design a comprehensive Phase II clinical trial protocol 
for CAR-T cell therapy in relapsed/refractory acute lymphoblastic leukemia, 
including background, design, endpoints, statistical analysis, and safety monitoring."""

# Example 2: Intervention description
prompt = """Describe a multi-component lifestyle intervention for 
patients with diabetes, including diet, exercise, behavioral change, 
and self-management education."""

# Example 3: Data analysis plan
prompt = """I am designing a randomized controlled trial with 
the primary endpoint being change in HbA1c at 12 months. 
Please help me develop a detailed statistical analysis plan, 
including primary analysis, secondary analysis, and sensitivity analysis."""

# Example 4: Research proposal writing
prompt = """Help me write the research design section of a research 
proposal on "AI-assisted early cancer diagnosis", including study type, 
study population, intervention, control settings, and expected outcomes."""

💡 Use Cases

1. Clinical Trial Design & Planning

Write complete trial protocols
Design trial endpoints and assessment metrics
Calculate sample size and statistical power
Develop randomization and blinding strategies
Create statistical analysis plans

2. Intervention Development

Design complex interventions
Describe intervention content and implementation methods
Develop dose-escalation protocols
Plan combination therapy studies
Evaluate intervention feasibility

3. Research Literature Review

Summarize intervention research evidence
Write systematic review methods
Synthesize results from multiple studies
Identify research gaps
Propose research recommendations

4. Research Paper Writing

Write methods sections
Describe intervention implementation processes
Explain statistical analysis methods
Present results
Generate discussion points

5. Data Analysis Support

Recommend appropriate statistical methods
Interpret analysis results
Plan subgroup analyses
Design sensitivity analyses
Handle missing data strategies

6. Regulatory & Ethics

Prepare ethics review materials
Write informed consent documents
Understand regulatory requirements
Plan safety reporting
Develop data monitoring plans

📊 Training Data

The model was fine-tuned on a curated English biomedical dataset:

Data Sources

Clinical Trial Databases: ClinicalTrials.gov, EU Clinical Trials Register
Biomedical Literature: English medical journals, PubMed abstracts, clinical guidelines
Research Methodology: English research design textbooks, statistical method guides, reporting standards (CONSORT, STROBE, etc.)
Professional Textbooks: Clinical epidemiology, biostatistics, evidence-based medicine

Data Characteristics

Training Samples: ~8,800 high-quality English intervention research data points
Training Epochs: 3 epochs
Data Quality: Professionally reviewed and quality controlled
Domain Coverage: Multiple therapeutic areas and research design types
Recency: Focus on 2018-2024 research content

⚠️ 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 (post-2024)
🔍 Domain boundaries: Performance optimized for biomedical intervention research; lower accuracy in other domains
🎯 Specialized focus: Better at clinical trials and intervention research than basic experimental research
🌐 Language: English-only; not suitable for multilingual or non-English research contexts

Ethical Guidelines

✅ Appropriate Uses

Academic research planning and design
Literature review and evidence synthesis
Research education and training
Protocol drafting and refinement
Statistical planning consultation
Regulatory guidance overview

❌ Inappropriate Uses

Clinical Decision-Making: Do not use for diagnosis, treatment, or patient management decisions
Direct Patient Care: Not intended for patient-facing applications
Regulatory Submissions: Should not be sole author of regulatory documents (human oversight required)
Automated Peer Review: Cannot replace human expert peer review
Medical Advice: Not a substitute for consultation with qualified healthcare professionals

🔒 Privacy & Security

No PHI/PII: Never input personally identifiable information or protected health information
Confidential Data: Do not input unpublished proprietary research data without proper safeguards
Patient Privacy: Always maintain compliance and patient confidentiality

📋 Verification Requirements

All generated content must be reviewed by qualified researchers/biostatisticians
Statistical calculations should be independently verified
Regulatory guidance should be confirmed with official sources
Clinical interpretations require expert validation

🎓 Academic Integrity

Treat as a research assistant tool, not an author
Always disclose AI assistance in research methods
Verify all factual claims and citations
Original critical thinking required for publication

🛠️ Technical Details

Inference Requirements

Minimum System Requirements

RAM: 32GB+ system memory
GPU: 24GB+ VRAM (e.g., RTX 4090, A5000)
Storage: ~50GB (model weights + cache)
Compute: CUDA-capable GPU (multi-GPU recommended)

Recommended Configuration

RAM: 64GB+ system memory
GPU: 2x A6000 or A100
Storage: 100GB SSD
OS: Linux with CUDA 12.1+

Performance Optimization

Memory Optimization

# Load with half precision
model = AutoModelForCausalLM.from_pretrained(
    "YangWu001/intervention_english_generator",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    low_cpu_mem_usage=True
)

# Optional: 8-bit quantization for further memory reduction
from transformers import BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = AutoModelForCausalLM.from_pretrained(
    "YangWu001/intervention_english_generator",
    quantization_config=quantization_config,
    device_map="auto"
)

Speed Optimization (using vLLM)

from vllm import LLM, SamplingParams

# Multi-GPU parallel inference
llm = LLM(
    model="YangWu001/intervention_english_generator",
    tensor_parallel_size=2,  # Use 2 GPUs
    dtype="bfloat16",
    gpu_memory_utilization=0.85,
    max_model_len=8192,
    trust_remote_code=True
)

# Efficient batch inference
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048
)

outputs = llm.generate(prompts, sampling_params)

📄 License

This model is released under the Apache License 2.0.

License Summary

✅ Permitted Uses

Commercial Use: Can be used in commercial products/services
Modification: Can be modified and adapted
Distribution: Can be redistributed
Patent Use: Grants patent rights from contributors
Private Use: Can be used privately

⚖️ Conditions

License and Copyright Notice: Must include license and copyright notice
State Changes: Must document significant modifications
Attribution: Must provide attribution to original authors

❌ Limitations

Liability: Provided "as-is" without warranty
Trademark Use: Does not grant trademark rights

Full license text: Apache License 2.0

🔗 Related Resources

Model Series

CoLabScience-EN (1B) - Small English research assistant
CoLabScience-CN-Generator (32B) - Chinese content generation model
Gemma3-12B-IT - Base model

Tools & Frameworks

Transformers - Hugging Face
vLLM - Efficient inference engine
PyTorch
LLaMA-Factory - Fine-tuning framework

📞 Contact

Model Author: Yang Wu
HuggingFace: @YangWu001
Model Repository: intervention_english_generator
Issue Reporting: Report Issues

🙏 Acknowledgments

This model builds upon the contributions of:

Base Models & Frameworks

Google Research for Gemma3 architecture and pre-training
Hugging Face for Transformers library and model hub infrastructure
PyTorch Team for deep learning framework
LLaMA-Factory for efficient fine-tuning tools

Data & Resources

ClinicalTrials.gov for clinical trial data
PubMed/NLM for biomedical literature access
Medical Journals for professional content
Open Source Community for tools and frameworks

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

Made with ❤️ for the biomedical research community

🤗 Model Hub • 📖 Documentation • 💬 Discussions • 🐛 Report Issues

Last Updated: March 2026

Downloads last month: 34

Safetensors

Model size

12B params

Tensor type

BF16

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support