Llama-2-7b-chat-hf_1bitgs8_hqq
| Property | Value |
|---|---|
| License | Llama2 |
| Architecture | 1-bit Quantized Llama2 |
| Paper | Research Paper |
| VRAM Usage | 1.85 GB |
What is Llama-2-7b-chat-hf_1bitgs8_hqq?
This is an experimental HQQ 1-bit quantized version of the Llama2-7B-chat model that employs binary weights and a low-rank adapter (HQQ+) to optimize performance while drastically reducing memory requirements. The model achieves significant memory reduction, requiring only 1.85GB of VRAM compared to the original 13.5GB, while maintaining reasonable performance across various benchmarks.
Implementation Details
The model implements an innovative quantization approach where weights are reduced to binary values (0 or 1), combined with a low-rank adapter (~94MB) to enhance performance. The dequantization process can be optimized as a 1-bit matrix multiplication, potentially requiring only additions and a very low-rank matrix multiplication for efficient computation.
- Utilizes unsigned 1-bit quantization (not ternary)
- Includes CPU-offloaded metadata
- Maintains binary weights and low-rank adapters in GPU memory
- Forward processing time of 0.257 seconds
Core Capabilities
- Text generation and chat functionality
- Reasonable performance on various benchmarks (37.56% average across standard tests)
- Efficient memory utilization with 7.3x reduction compared to FP16
- Specialized performance on mathematical and general knowledge tasks
Frequently Asked Questions
Q: What makes this model unique?
This model represents a significant advancement in extreme quantization, achieving 1-bit precision while maintaining usable performance through innovative use of low-rank adapters. It's particularly notable for achieving this with a relatively small model like Llama2-7B, which is traditionally challenging to quantize effectively.
Q: What are the recommended use cases?
The model is best suited for scenarios where memory efficiency is crucial, such as deployment on resource-constrained devices or when running multiple instances. It performs reasonably well on general chat tasks, mathematical problems, and knowledge-based queries, though with some performance trade-off compared to the full-precision model.
