NeuralDaredevil-12b-32k-GGUF
| Property | Value |
|---|---|
| Parameter Count | 12.5B |
| Model Type | GGUF Quantized Transformer |
| Base Model | mvpmaster/NeuralDaredevil-12b-32k |
| Author | mradermacher |
What is NeuralDaredevil-12b-32k-GGUF?
NeuralDaredevil-12b-32k-GGUF is a quantized version of the NeuralDaredevil 12B parameter model, specifically optimized for efficient deployment and reduced memory footprint. This model offers various quantization options ranging from 4.7GB to 13.4GB, making it suitable for different hardware configurations and performance requirements.
Implementation Details
The model provides multiple quantization variants, each optimized for different use cases. The quantization types include Q2_K (4.7GB) for minimal size, through to Q8_0 (13.4GB) for maximum quality. Notable implementations include the recommended Q4_K_S and Q4_K_M variants, which offer an excellent balance between performance and quality.
- Extended context window of 32k tokens
- Multiple quantization options for different performance needs
- Optimized GGUF format for efficient inference
- Support for various deployment scenarios
Core Capabilities
- Efficient memory utilization through various quantization options
- Fast inference capabilities, especially with K-quant variants
- Support for extended context understanding
- Flexible deployment options for different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its variety of quantization options, allowing users to choose between extreme compression (Q2_K at 4.7GB) and high quality (Q8_0 at 13.4GB) based on their specific needs. The K-quant variants offer particularly good performance-to-size ratios.
Q: What are the recommended use cases?
For most applications, the Q4_K_S (7.2GB) or Q4_K_M (7.6GB) variants are recommended as they offer a good balance of speed and quality. For scenarios requiring maximum quality, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the lighter Q2_K or Q3_K variants.
