Llama-3.1-8B-Instruct-Jopara-V3.2-GGUF
| Property | Value |
|---|---|
| Base Model | Llama 3.1 8B |
| Model Type | GGUF Quantized |
| Author | mradermacher |
| Original Source | rubuntu/Llama-3.1-8B-Instruct-Jopara-V3.2 |
What is Llama-3.1-8B-Instruct-Jopara-V3.2-GGUF?
This is a specialized quantized version of the Llama 3.1 8B model, specifically optimized for the Jopara language. The model offers various quantization levels to balance between model size and performance, ranging from highly compressed 3.3GB versions to full 16.2GB implementations.
Implementation Details
The model provides multiple quantization options utilizing GGUF format, each optimized for different use cases. The quantization types include Q2_K through Q8_0, with specific optimizations for performance and quality trade-offs.
- Q4_K_S and Q4_K_M variants (4.8-5.0GB) are recommended for general use, offering good balance of speed and quality
- Q6_K (6.7GB) provides very good quality with moderate size
- Q8_0 (8.6GB) offers the best quality while maintaining reasonable speed
- F16 (16.2GB) provides full precision but is typically overkill for most applications
Core Capabilities
- Efficient compression options ranging from 3.3GB to 16.2GB
- Optimized for Jopara language processing
- Multiple quantization levels for different performance needs
- Fast inference capabilities with recommended Q4_K variants
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for Jopara language while offering various quantization options to suit different deployment scenarios, from lightweight to full-precision implementations.
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 applications requiring highest quality, the Q8_0 variant is recommended.
