Guanaco-65B-GPTQ
| Property | Value |
|---|---|
| Original Author | Tim Dettmers |
| Quantization Author | TheBloke |
| Model Size | 65B parameters |
| Quantization Options | 3-bit and 4-bit |
| Hugging Face Repo | TheBloke/guanaco-65B-GPTQ |
What is guanaco-65B-GPTQ?
Guanaco-65B-GPTQ is a quantized version of Tim Dettmers' Guanaco 65B model, optimized for efficient GPU inference. This implementation provides multiple GPTQ quantization options, allowing users to balance between model size, VRAM usage, and inference quality. The model maintains the original capabilities while significantly reducing the memory footprint through various quantization configurations.
Implementation Details
The model offers multiple quantization parameters through different branches, with options including 3-bit and 4-bit precision, various group sizes (32g, 64g, 128g), and Act-Order configurations. The quantization process utilized the WikiText dataset with a sequence length of 2048 tokens.
- 4-bit variants range from 33.49GB to 38.53GB in size
- 3-bit variants range from 25.39GB to 27.78GB in size
- Compatible with AutoGPTQ, Transformers, and some variants work with ExLlama
- Includes optimized configurations for different VRAM constraints
Core Capabilities
- Multiple quantization options for different hardware requirements
- ExLlama compatibility for 4-bit variants
- Supports both inference methods: direct Transformers pipeline and AutoGPTQ
- Maintains original model capabilities while reducing resource requirements
- Flexible deployment options with various group sizes and configurations
Frequently Asked Questions
Q: What makes this model unique?
This implementation offers unprecedented flexibility in deployment through multiple quantization options, allowing users to choose the optimal configuration based on their hardware constraints and quality requirements. The variety of GPTQ parameters and group sizes makes it highly adaptable to different use cases.
Q: What are the recommended use cases?
The model is ideal for GPU inference scenarios where VRAM optimization is crucial. The 4-bit variants with Act-Order are recommended for highest quality inference, while 3-bit variants are suitable for more constrained environments. Users can select the appropriate quantization based on their specific balance of quality versus resource requirements.
