OopsHusBot-3B-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Size | 3B parameters |
| Original Source | alpha-ai/OopsHusBot-3B |
| Format | GGUF (Various Quantizations) |
What is OopsHusBot-3B-GGUF?
OopsHusBot-3B-GGUF is a quantized version of the original OopsHusBot-3B model, optimized for efficient deployment while maintaining performance. This implementation offers multiple quantization options ranging from 1.5GB to 6.5GB, allowing users to balance between model size and quality based on their specific needs.
Implementation Details
The model features various quantization types, including standard and IQ (Improved Quantization) variants. Notable quantization options include Q4_K_S and Q4_K_M which are recommended for their balance of speed and quality, Q6_K for very good quality, and Q8_0 for the best quality at a larger size.
- Q2_K (1.5GB) - Smallest size option
- Q4_K_S/M (2.0-2.1GB) - Recommended for general use
- Q6_K (2.7GB) - High-quality option
- Q8_0 (3.5GB) - Best quality quantization
- F16 (6.5GB) - Full precision, uncompressed version
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Optimized for various compute environments
- Compatible with standard GGUF loading implementations
- Supports both static and weighted/imatrix quantizations
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, allowing users to choose the optimal trade-off between model size and performance for their specific use case. The availability of both standard and IQ-quants provides additional flexibility.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M quantizations (2.0-2.1GB) are recommended as they offer a good balance of speed and quality. For scenarios requiring maximum quality, the Q8_0 version is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K variants.
