BuddyGlassUncensored2025.6-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Type | GGUF Quantized Language Model |
| Source | darkc0de/BuddyGlassUncensored2025.6 |
| Available Formats | Multiple GGUF quantizations |
What is BuddyGlassUncensored2025.6-GGUF?
BuddyGlassUncensored2025.6-GGUF is a specialized quantized version of the original BuddyGlass model, optimized for efficient deployment and inference. It offers multiple quantization options to balance between model size, performance, and quality requirements.
Implementation Details
The model provides various quantization formats ranging from Q2_K (9.0GB) to Q8_0 (25.2GB), each optimized for different use cases. Notable implementations include IQ4_XS (13.0GB) and the recommended Q4_K_S (13.6GB) and Q4_K_M (14.4GB) variants for optimal performance.
- Q2_K through Q8_0 quantization options available
- IQ-quants (weighted/imatrix) versions available separately
- Optimized for different size/quality trade-offs
- Fast inference options with Q4_K variants
Core Capabilities
- Flexible deployment options with multiple quantization levels
- Optimized memory usage (9.0GB to 25.2GB)
- High-quality preservation with Q6_K and Q8_0 variants
- Fast inference capability with recommended Q4_K variants
Frequently Asked Questions
Q: What makes this model unique?
This model offers a comprehensive range of quantization options, allowing users to choose the optimal balance between model size and performance. The availability of both standard and IQ-quants provides flexibility for different deployment scenarios.
Q: What are the recommended use cases?
For optimal performance with reasonable size requirements, the Q4_K_S (13.6GB) and Q4_K_M (14.4GB) variants are recommended. For highest quality needs, the Q8_0 (25.2GB) variant is suggested, while Q2_K (9.0GB) is suitable for resource-constrained environments.
