Bio-Medical-MultiModal-Llama-3-8B-V1
| Property | Value |
|---|---|
| Parameter Count | 8 billion |
| Base Model | Llama-3-8B-Instruct |
| Training Data | 500,000+ biomedical entries |
| License | Non-Commercial Use Only |
| Framework | PyTorch 2.1.2, Transformers 4.40.2 |
What is Bio-Medical-MultiModal-Llama-3-8B-V1?
Bio-Medical-MultiModal-Llama-3-8B-V1 is an advanced multimodal language model specifically designed for biomedical applications. Built upon Meta's Llama-3-8B-Instruct architecture, this model has been fine-tuned on a comprehensive dataset of over 500,000 biomedical entries, combining both text and image processing capabilities. The model represents a significant advancement in medical AI, capable of understanding and analyzing medical imagery while generating relevant textual insights.
Implementation Details
The model utilizes Flash Attention 2 and supports 4-bit quantization for efficient deployment. Training was conducted on NVIDIA H100 GPUs using MiniCPM for multimodal processing, with carefully selected hyperparameters including a learning rate of 0.0002 and cosine scheduler with warmup. The implementation supports both image and text inputs, making it versatile for various medical applications.
- Trained using Native AMP mixed precision
- Supports 4-bit quantization for efficient deployment
- Implements Flash Attention 2 for improved performance
- Uses MiniCPM for multimodal data processing
Core Capabilities
- Medical image analysis and interpretation
- Biomedical research support and literature review
- Clinical decision support systems
- Educational support for medical professionals
- Comprehensive analysis of medical modalities and organs
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines multimodal capabilities with specialized biomedical knowledge, allowing it to process both medical images and text simultaneously. Its training on a diverse dataset of 500,000+ entries ensures broad coverage of medical knowledge.
Q: What are the recommended use cases?
The model is ideal for research support, clinical decision support, and medical education. It can analyze medical images, provide detailed reports, and assist in literature review. However, it should be used as a supporting tool rather than a primary diagnostic system.
