Bio-Medical-Llama-3-8B
| Property | Value |
|---|---|
| Base Model | Llama-3-8B-Instruct |
| Parameter Count | 8 billion |
| Training Dataset Size | 500,000+ entries |
| License | Non-Commercial Use Only |
| Author | ContactDoctor |
What is Bio-Medical-Llama-3-8B?
Bio-Medical-Llama-3-8B is a specialized large language model fine-tuned specifically for biomedical applications. Built upon Meta's Llama-3-8B-Instruct architecture, this model has been trained on a comprehensive dataset of over 500,000 entries, combining both synthetic and manually curated biomedical data. The model demonstrates superior performance across various medical evaluation metrics, including medmcqa, medqa_4options, and multiple MMLU medical subtasks.
Implementation Details
The model was trained using carefully selected hyperparameters, including a learning rate of 0.0002, mixed precision training with Native AMP, and the Adam optimizer. The training process utilized a cosine learning rate scheduler with a warmup ratio of 0.03 and was conducted over 2000 training steps.
- Training batch size: 32 (effective)
- Gradient accumulation steps: 4
- Framework versions: PEFT 0.11.0, Transformers 4.40.2, PyTorch 2.1.2
Core Capabilities
- Research Support: Assists in biomedical literature review and data extraction
- Clinical Decision Support: Provides information for medical decision-making
- Educational Tool: Serves as a learning resource for medical professionals
- Natural Language Understanding: Processes and generates biomedical text
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized training on a vast biomedical dataset, combining synthetic and curated data to ensure comprehensive coverage of medical knowledge. It's specifically optimized for healthcare applications while maintaining the powerful capabilities of the Llama-3 architecture.
Q: What are the recommended use cases?
The model is ideal for medical research assistance, clinical decision support, and medical education. However, it should be used as a complementary tool rather than a replacement for professional medical judgment, with special attention to verifying critical information from reliable sources.
