ReaderLM-v2-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Original Model | jinaai/ReaderLM-v2 |
| Format | GGUF |
| Size Range | 0.9GB - 3.7GB |
What is ReaderLM-v2-GGUF?
ReaderLM-v2-GGUF is a collection of quantized versions of the original ReaderLM-v2 model, optimized for different use cases and hardware constraints. This implementation provides various quantization levels, allowing users to choose the optimal balance between model size and performance.
Implementation Details
The model comes in multiple quantization variants, each optimized for different use cases:
- Q2_K: Smallest size at 0.9GB
- Q4_K_S/M: Fast and recommended variants at 1.2GB
- Q6_K: Very good quality at 1.6GB
- Q8_0: Highest quality practical variant at 2.0GB
- F16: Full precision at 3.7GB
Core Capabilities
- Multiple quantization options for different hardware constraints
- Size-optimized variants from 0.9GB to 3.7GB
- IQ-quants available for better quality/size ratio
- Compatible with standard GGUF loading tools
Frequently Asked Questions
Q: What makes this model unique?
This model offers a comprehensive range of quantization options, allowing users to choose the perfect balance between model size and quality. The availability of IQ-quants provides better quality for similar sizes compared to standard quantization.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants (1.2GB) are recommended as they offer a good balance of speed and quality. For highest quality needs, Q8_0 (2.0GB) is recommended, while Q2_K (0.9GB) is suitable for very constrained environments.
