law-LLM
| Property | Value |
|---|---|
| Parameter Count | 6.74B |
| Base Model | LLaMA-1-7B |
| Research Paper | Link |
| Tensor Type | F32/FP16 |
| Training Datasets | OpenOrca, LIMA, WizardLM, The Pile |
What is law-LLM?
law-LLM is a specialized legal domain language model developed through continuous pre-training of LLaMA-1-7B using an innovative reading comprehension approach. This model represents a significant advancement in domain-specific AI, competing with much larger models like BloombergGPT-50B while maintaining a smaller parameter count.
Implementation Details
The model employs a unique methodology that transforms large-scale pre-training corpora into reading comprehension texts, addressing the common challenge of maintaining prompting ability while incorporating domain knowledge. It's implemented using PyTorch and supports both F32 and FP16 precision.
- Continuous pre-training on legal domain corpora
- Reading comprehension-based training methodology
- Compatible with text-generation-inference endpoints
- Supports both base model and chat model variants
Core Capabilities
- Advanced legal domain knowledge processing
- Efficient text generation for legal queries
- Multi-format support (base model and chat model versions)
- Competitive performance against larger domain-specific models
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its reading comprehension-based training approach, which allows it to maintain strong prompting abilities while incorporating specialized legal knowledge. This methodology has proven effective across different model scales and architectures.
Q: What are the recommended use cases?
law-LLM is particularly suited for legal domain tasks, including legal question answering, document analysis, and legal research assistance. For interactive applications, it's recommended to use the chat model variant for better response quality.
