Llama-2-70B-Chat-GPTQ
| Property | Value |
|---|---|
| Parameter Count | 70 Billion |
| License | Llama2 |
| Research Paper | Link |
| Model Type | GPTQ-Quantized Chat Model |
| Author | TheBloke |
What is Llama-2-70B-Chat-GPTQ?
Llama-2-70B-Chat-GPTQ is a quantized version of Meta's Llama 2 70B chat model, optimized for efficient deployment while maintaining performance. This model represents a significant advancement in making large language models more accessible, offering multiple quantization options to balance between performance and resource requirements.
Implementation Details
The model features various GPTQ quantization configurations, ranging from 3-bit to 4-bit precision with different group sizes. The implementation includes multiple branches optimized for different use cases, with file sizes ranging from 26.78GB to 40.66GB depending on the quantization parameters.
- Multiple quantization options (3-bit and 4-bit precision)
- Various group sizes (32g, 64g, 128g) for different VRAM requirements
- Compatible with AutoGPTQ and ExLlama (4-bit versions)
- Optimized for dialogue use cases with comprehensive prompt template
Core Capabilities
- Advanced dialogue generation with safety-focused responses
- Comprehensive knowledge across various domains
- Strong performance on academic benchmarks (68.9% on MMLU)
- Efficient resource utilization through quantization
- Support for context length up to 4096 tokens
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful capabilities of Llama 2 70B with efficient quantization, making it possible to run a state-of-the-art language model on consumer hardware while maintaining high performance. The multiple quantization options allow users to choose the best configuration for their specific hardware constraints.
Q: What are the recommended use cases?
The model excels in dialogue-based applications, including chat interactions, question-answering, and content generation. It's particularly well-suited for applications requiring both high performance and efficient resource usage, with specific optimizations for safety and helpfulness in responses.
