watt-tool-8B-GGUF
| Property | Value |
|---|---|
| Original Model | watt-ai/watt-tool-8B |
| Author | mradermacher |
| Model Size Range | 3.3GB - 16.2GB |
| Repository | Hugging Face |
What is watt-tool-8B-GGUF?
watt-tool-8B-GGUF is a quantized version of the original watt-tool-8B model, optimized for efficient deployment and reduced memory footprint. This implementation offers multiple quantization options, allowing users to balance between model size and performance based on their specific needs.
Implementation Details
The model provides various quantization types, from highly compressed Q2_K (3.3GB) to full precision f16 (16.2GB). Notable implementations include the recommended Q4_K_S and Q4_K_M variants, which offer an excellent balance between speed and quality, and the Q8_0 variant which provides the highest quality while maintaining reasonable size requirements.
- Multiple quantization options ranging from Q2_K to f16
- IQ-quants available for enhanced performance
- Optimized versions for different use-cases
- Weighted/imatrix quants available in separate repository
Core Capabilities
- Flexible deployment options with various size/quality trade-offs
- Fast inference with recommended Q4_K variants
- High-quality output with Q6_K and Q8_0 variants
- Compatible with standard GGUF loading tools
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, allowing users to choose the perfect balance between model size, inference speed, and output quality. The availability of both standard and IQ-quants provides additional flexibility for different deployment scenarios.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they provide a good balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K variants.
