oh-dcft-v3.1-gemini-1.5-pro-i1-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Original Model | mlfoundations-dev/oh-dcft-v3.1-gemini-1.5-pro |
| Model Format | GGUF (Various Quantizations) |
| Repository | Hugging Face |
What is oh-dcft-v3.1-gemini-1.5-pro-i1-GGUF?
This is a specialized quantized version of the oh-dcft-v3.1-gemini-1.5-pro model, offering various GGUF formats optimized for different use cases. The model provides multiple quantization options ranging from 2.1GB to 6.7GB, allowing users to choose the optimal balance between model size, inference speed, and output quality.
Implementation Details
The implementation features both weighted/imatrix quantizations and static quantizations, with file sizes carefully optimized for different deployment scenarios. The model offers various quantization types including IQ (Imatrix) and standard quantization formats like Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K.
- Multiple quantization options from IQ1_S (2.1GB) to Q6_K (6.7GB)
- Optimized imatrix quantization for better quality/size ratio
- Recommended Q4_K_M variant (5.0GB) for optimal speed/quality balance
- Various compression levels suitable for different hardware configurations
Core Capabilities
- Efficient model deployment with minimal quality loss
- Flexible size options for various hardware constraints
- Optimized performance with imatrix quantization technology
- Compatible with standard GGUF loaders and frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, particularly the imatrix quantizations that often provide better quality than similar-sized standard quants. It offers exceptional flexibility in deployment, from ultra-lightweight 2.1GB versions to high-quality 6.7GB variants.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (5.0GB) is recommended as it provides the best balance of speed and quality. For resource-constrained environments, the IQ3 variants offer good quality at smaller sizes. The Q6_K variant is recommended for applications requiring maximum quality.
