Qwen2-VL-OCR-2B-Instruct-i1-GGUF
| Property | Value |
|---|---|
| Original Model | Qwen2-VL-OCR-2B-Instruct |
| Author | mradermacher |
| Format | GGUF |
| Size Range | 0.5GB - 1.4GB |
| Repository | Hugging Face |
What is Qwen2-VL-OCR-2B-Instruct-i1-GGUF?
This is a quantized version of the Qwen2-VL-OCR-2B-Instruct model, optimized for efficient deployment using the GGUF format. The repository provides multiple quantization variants, offering different trade-offs between model size, speed, and quality.
Implementation Details
The model implements both weighted and imatrix quantization techniques, providing various compression levels from IQ1 to Q6_K. Each variant is carefully optimized to maintain a balance between model size and performance.
- Multiple quantization options ranging from 0.5GB to 1.4GB
- IQ-quants (imatrix) generally offer better quality than similar-sized non-IQ variants
- Optimized for different use cases and hardware constraints
Core Capabilities
- Efficient model deployment with reduced size requirements
- Multiple compression options for different needs
- Maintains OCR and visual-language capabilities of the original model
- Optimal variants for different speed/quality trade-offs
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the Qwen2-VL-OCR-2B-Instruct model, with special attention to imatrix quantization for better quality at smaller sizes. It offers various compression levels suitable for different deployment scenarios.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M variant (1.1GB) is recommended as it provides a good balance of speed and quality. For more constrained environments, IQ3_M (0.9GB) offers good quality at a smaller size. The Q6_K variant (1.4GB) provides quality closest to the original model.
