Open-Insurance-LLM-Llama3-8B
| Property | Value |
|---|---|
| Parameter Count | 8.05B |
| Model Type | Instruction-tuned Language Model |
| Base Model | nvidia/Llama3-ChatQA-1.5-8B |
| License | llama3 |
| Training Method | LoRA (8-bit) |
What is Open-Insurance-LLM-Llama3-8B?
Open-Insurance-LLM-Llama3-8B is a specialized language model designed specifically for insurance-related applications. Built on the Llama 3 architecture and fine-tuned using the InsuranceQA dataset, this model represents a significant advancement in domain-specific AI for insurance tasks. It employs LoRA optimization with 20.97M trainable parameters, representing 0.26% of its total 8.05B parameters.
Implementation Details
The model leverages advanced training techniques including LoRA configuration with r=8, lora_alpha=32, and targeted fine-tuning of specific modules including projection layers (up_proj, down_proj, gate_proj) and attention components (k_proj, q_proj, v_proj, o_proj). The model is distributed in FP16 format across multiple safetensors files for efficient loading and deployment.
- Enhanced attention mechanisms from Llama 3 architecture
- ChatQA 1.5 instruction-tuning framework integration
- Specialized insurance domain adaptations
- Efficient parameter distribution with safetensors
Core Capabilities
- Insurance policy interpretation and explanation
- Claims processing assistance and guidance
- Coverage analysis and assessment
- Insurance terminology clarification
- Policy comparison and recommendations
- Risk assessment processing
- Compliance question handling
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful Llama 3 architecture with insurance-specific training, making it particularly effective for insurance-related queries while maintaining the general capabilities of a large language model. The use of LoRA optimization allows for efficient fine-tuning while preserving the base model's knowledge.
Q: What are the recommended use cases?
The model is ideal for insurance professionals, customer service applications, and automated insurance query systems. However, it should be used as an assistive tool rather than a replacement for professional insurance advice, with output verification by qualified insurance professionals.
