Llama-Doctor-3.2-3B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Base Model | Meta Llama-3.2-3B-Instruct |
| License | MIT |
| Framework | PyTorch |
| Training Dataset | avaliev/chat_doctor |
What is Llama-Doctor-3.2-3B-Instruct?
Llama-Doctor-3.2-3B-Instruct is a specialized language model built on Meta's Llama 3.2 architecture, fine-tuned specifically for medical consultation and healthcare-related conversations. This 3-billion parameter model leverages the chat_doctor dataset to provide informed responses to medical queries while maintaining the instruction-following capabilities of the base Llama model.
Implementation Details
The model is implemented using PyTorch and comes with comprehensive configuration files for easy deployment. It utilizes a sophisticated tokenizer system with a 17.2MB vocabulary file and includes specialized configurations for generation tasks. The model is distributed across two PyTorch files totaling approximately 6.43GB.
- Full PyTorch implementation with safetensors support
- Specialized tokenizer configuration for medical terminology
- Optimized for both CPU and GPU deployment
- Available in GGUF format for efficient inference
Core Capabilities
- Medical consultation and healthcare advice generation
- Instruction-following in clinical contexts
- Conversational AI for healthcare applications
- General text generation with medical expertise
- Technical and academic content creation in healthcare domains
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Llama 3.2 architecture with specialized medical knowledge from the chat_doctor dataset, making it particularly effective for healthcare-related applications while maintaining general instruction-following capabilities.
Q: What are the recommended use cases?
The model excels in medical consultation chatbots, healthcare content generation, patient information systems, and technical medical documentation. It's designed for both conversational interactions and structured medical information delivery.
