OREAL-32B-GGUF
| Property | Value |
|---|---|
| Original Model | InternLM OREAL-32B |
| Author | mradermacher |
| Format | GGUF (Various Quantizations) |
| Model Repository | Hugging Face |
What is OREAL-32B-GGUF?
OREAL-32B-GGUF is a comprehensive collection of quantized versions of the original InternLM OREAL-32B model, optimized for different use cases and hardware configurations. The quantization process reduces the model's size while maintaining varying degrees of performance, making it more accessible for deployment on different hardware setups.
Implementation Details
The model offers multiple quantization options, ranging from the lightweight Q2_K (12.4GB) to the high-quality Q8_0 (34.9GB). Notable implementations include the recommended Q4_K_S (18.9GB) and Q4_K_M (20.0GB) variants, which provide an excellent balance between speed and quality.
- Multiple quantization types available (Q2_K through Q8_0)
- IQ4_XS option for specialized use cases
- Weighted/imatrix variants available separately
- Size options ranging from 12.4GB to 34.9GB
Core Capabilities
- Fast inference with recommended Q4_K variants
- High-quality output with Q6_K and Q8_0 quantizations
- Flexible deployment options for different hardware configurations
- Optimized memory usage while maintaining model performance
Frequently Asked Questions
Q: What makes this model unique?
The model provides a comprehensive range of quantization options, allowing users to choose the optimal balance between model size, inference speed, and output quality. The availability of both standard and IQ-quants makes it versatile for different use cases.
Q: What are the recommended use cases?
For most applications, the Q4_K_S (18.9GB) and Q4_K_M (20.0GB) variants are recommended as they offer fast inference while maintaining good quality. For highest quality requirements, the Q8_0 (34.9GB) variant is recommended, while resource-constrained environments might benefit from the lighter Q2_K or Q3_K_S variants.
