Granite 3.2 8B Instruct Abliterated GGUF
| Property | Value |
|---|---|
| Base Model | Granite 3.2 8B |
| Model Type | Instruction-tuned Language Model |
| Size Range | 3.2GB - 16.4GB (various quantizations) |
| Author | mradermacher |
| Repository | Hugging Face |
What is granite-3.2-8b-instruct-abliterated-GGUF?
This is a quantized version of the Granite 3.2 8B instruction model, specifically optimized for efficient deployment through GGUF format. It offers multiple quantization options to balance between model size and performance, ranging from lightweight 3.2GB versions to full 16.4GB implementations.
Implementation Details
The model provides various quantization types, each optimized for different use cases. The most notable implementations include Q4_K_S and Q4_K_M which are recommended for their balance of speed and quality, while Q8_0 offers the highest quality at 8.8GB.
- Q2_K: Smallest size at 3.2GB
- Q4_K_S/M: Recommended for balanced performance (4.8-5.0GB)
- Q6_K: Very good quality at 6.8GB
- Q8_0: Best quality at 8.8GB
- F16: Full precision at 16.4GB
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Optimized for instruction-following tasks
- Efficient memory usage through GGUF format
- Compatible with various inference frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its variety of quantization options, allowing users to choose the perfect balance between model size and performance for their specific use case. The availability of IQ-quants (like IQ4_XS) offers additional optimization options.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M quantizations are recommended as they offer a good balance of speed and quality. For highest quality requirements, Q8_0 is recommended, while Q2_K is suitable for resource-constrained environments.
