Ice0.41-22.11-RP-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| License | CC-BY-NC-4.0 |
| Author | mradermacher |
| Base Model | icefog72/Ice0.41-22.11-RP |
What is Ice0.41-22.11-RP-GGUF?
Ice0.41-22.11-RP-GGUF is a sophisticated quantized version of the Ice0.41-22.11-RP model, optimized for efficient deployment and inference. This model represents a significant advancement in making large language models more accessible and practical for various deployment scenarios through different quantization options.
Implementation Details
The model offers an impressive range of quantization options, from lightweight 2.8GB implementations to full 14.6GB versions. Notable quantization variants include:
- Q2_K (2.8GB) - Smallest size option
- Q4_K_S/M (4.2-4.5GB) - Fast and recommended for general use
- Q6_K (6.0GB) - Very good quality balance
- Q8_0 (7.8GB) - Highest quality practical implementation
- F16 (14.6GB) - Full precision, maximum quality
Core Capabilities
- Optimized for conversational AI applications
- Multiple quantization options for different hardware constraints
- Supports English language processing
- Efficient inference endpoints integration
- Compatible with transformers library
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its versatile quantization options, allowing users to choose between different size-quality tradeoffs, from extremely compressed versions to full precision implementations.
Q: What are the recommended use cases?
The model is particularly well-suited for conversational AI applications where deployment efficiency is crucial. The Q4_K_S and Q4_K_M variants are recommended for general use, offering an optimal balance between performance and resource utilization.
