Cakrawala-8B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | MIT |
| Base Model | NarrativAI/Cakrawala-8B |
| Format | GGUF (Various Quantizations) |
What is Cakrawala-8B-GGUF?
Cakrawala-8B-GGUF is a quantized version of the original Cakrawala-8B model, optimized for efficient deployment and reduced memory footprint. This implementation offers multiple quantization variants ranging from 3.3GB to 16.2GB, providing flexibility for different hardware configurations and use-case requirements.
Implementation Details
The model is available in various quantization formats, with notable options including Q4_K_S and Q4_K_M (recommended for fast performance), Q6_K for very good quality, and Q8_0 for best quality results. The implementation supports different precision levels, from lightweight Q2_K (3.3GB) to full F16 precision (16.2GB).
- Multiple quantization options for different performance/quality trade-offs
- Optimized for conversational tasks using the transformers architecture
- Supports English language processing
- Compatible with axolotl framework
Core Capabilities
- Efficient inference with reduced memory footprint
- Flexible deployment options through various quantization levels
- Optimized for both CPU and GPU execution
- Suitable for production environments with MIT license compliance
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its variety of quantization options, allowing users to choose the optimal balance between model size, inference speed, and output quality. It's particularly notable for including both standard and IQ-quantized versions.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they offer 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 Q2_K or Q3_K_S variants.
