Tulu-3.1-8B-SuperNova-i1-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Transformer |
| Base Model | bunnycore/Tulu-3.1-8B-SuperNova |
| Language | English |
What is Tulu-3.1-8B-SuperNova-i1-GGUF?
This is a quantized version of the Tulu-3.1-8B-SuperNova model, optimized for efficient deployment using the GGUF format. It offers multiple quantization variants to balance between model size, inference speed, and quality.
Implementation Details
The model provides various quantization options ranging from 2.1GB to 6.7GB in size. It implements imatrix quantization techniques, offering superior quality compared to traditional quantization methods.
- Multiple quantization variants (IQ1 through Q6_K)
- Size options from 2.1GB to 6.7GB
- Optimized for different hardware configurations
- Enhanced with imatrix technology
Core Capabilities
- Efficient memory usage with various compression levels
- Optimized performance for different hardware setups (ARM, SVE)
- Balanced quality-to-size ratio options
- Specialized variants for resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options, particularly the IQ (imatrix) variants that often provide better quality than traditional quantization at similar sizes.
Q: What are the recommended use cases?
For general use, the Q4_K_M variant (5.0GB) is recommended as it offers a good balance of speed and quality. For resource-constrained systems, the IQ3 variants provide good quality at smaller sizes.
