Guanaco-33B-GPTQ
| Property | Value |
|---|---|
| Base Model | LLaMA |
| Parameters | 33B |
| License | Other |
| Quantization | GPTQ (Multiple variants) |
| Author | TheBloke (Quantized) / Tim Dettmers (Original) |
What is guanaco-33B-GPTQ?
Guanaco-33B-GPTQ is a quantized version of Tim Dettmers' Guanaco 33B model, optimized for efficient deployment while maintaining performance. The model comes in multiple GPTQ variants ranging from 3-bit to 8-bit precision, allowing users to balance between model size and performance based on their hardware constraints.
Implementation Details
The model implements various quantization configurations, with the main branch offering 4-bit precision with Act Order and no group size to minimize VRAM requirements. It uses the Wikitext dataset for quantization with a sequence length of 2048 tokens.
- Multiple GPTQ variants available (3-bit to 8-bit)
- Different group sizes (None, 32g, 64g, 128g) for various VRAM optimization levels
- Compatibility with ExLlama for 4-bit versions
- Supports text-generation-inference and various GPTQ implementations
Core Capabilities
- High-quality text generation with optimized memory usage
- Flexible deployment options for different hardware configurations
- Compatible with popular frameworks like AutoGPTQ and Transformers
- Supports both Python API and text-generation-webui integration
Frequently Asked Questions
Q: What makes this model unique?
This model offers exceptional flexibility with multiple quantization options, allowing users to choose the optimal balance between model size and performance. The various GPTQ configurations make it suitable for a wide range of hardware setups.
Q: What are the recommended use cases?
The model is ideal for production deployments where efficient resource usage is crucial. The 4-bit variants are recommended for most users, while 3-bit options are suitable for limited VRAM scenarios, and 8-bit for cases requiring higher precision.
