Granite 3.2 8B Instruct Abliterated GGUF
| Property | Value |
|---|---|
| Base Model Size | 8B parameters |
| Model Type | Instruction-tuned Language Model |
| Format | GGUF Quantized |
| Author | mradermacher |
| Source | huggingface.co/huihui-ai/granite-3.2-8b-instruct-abliterated |
What is granite-3.2-8b-instruct-abliterated-i1-GGUF?
This is a specialized quantized version of the Granite 3.2 8B instruction-tuned language model, optimized for different deployment scenarios. The model comes in various GGUF formats, ranging from highly compressed 1.9GB versions to high-quality 6.8GB implementations, allowing users to choose the optimal balance between model size and performance for their specific use case.
Implementation Details
The model features multiple quantization variants using both standard and IQ (imatrix) quantization techniques. The implementation includes various compression levels, from IQ1 to Q6_K, each offering different trade-offs between model size, inference speed, and output quality.
- Multiple quantization options (IQ1_S through Q6_K)
- Size range from 1.9GB to 6.8GB
- Optimized imatrix quantization variants
- Various compression levels for different use cases
Core Capabilities
- Efficient deployment with minimal quality loss in higher quantization levels
- Flexible size options for different hardware constraints
- Optimal performance with Q4_K_M variant (5.0GB) recommended for balanced usage
- Support for instruction-following tasks
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, including innovative imatrix quantization, allowing users to precisely balance size, speed, and quality requirements. The Q4_K_M variant at 5.0GB is particularly notable for offering an optimal balance of speed and quality.
Q: What are the recommended use cases?
For most applications, the Q4_K_M variant (5.0GB) is recommended as it provides a good balance of speed and quality. For resource-constrained environments, the IQ3 variants offer reasonable performance at smaller sizes, while the Q6_K variant is suitable for applications requiring maximum quality.
