OlympicCoder-32B-GGUF
| Property | Value |
|---|---|
| Base Model | OlympicCoder-32B |
| Parameter Count | 32 Billion |
| Model Type | GGUF Quantized LLM |
| Original Source | open-r1/OlympicCoder-32B |
| Quantization Range | 9GB - 35GB |
What is open-r1_OlympicCoder-32B-GGUF?
OlympicCoder-32B-GGUF is a comprehensive collection of quantized versions of the original OlympicCoder-32B model, specifically optimized for efficient deployment using llama.cpp. The collection offers 26 different quantization variants, ranging from the highest quality Q8_0 (34.82GB) to the most compressed IQ2_XXS (9.03GB), allowing users to balance quality and resource requirements.
Implementation Details
The model uses imatrix quantization techniques and offers various specialized formats including K-quants and I-quants. Each variant is optimized for specific hardware configurations and use cases, with special consideration for embedding and output weight handling in certain versions.
- Utilizes llama.cpp release b4867 for quantization
- Supports online repacking for ARM and AVX CPU inference
- Implements SOTA techniques for lower bit-depth quantization
- Features special Q8_0 embedding handling in certain variants
Core Capabilities
- Efficient code generation and technical task processing
- Flexible deployment options across different hardware configurations
- Optimized performance through specialized quantization techniques
- Support for both CPU and GPU acceleration
Frequently Asked Questions
Q: What makes this model unique?
The model offers an unprecedented range of quantization options, from extremely high-quality 34.82GB versions to highly compressed 9GB variants, while maintaining usability even in lower bit-depth formats through advanced quantization techniques.
Q: What are the recommended use cases?
For most general use cases, the Q4_K_M variant (19.85GB) is recommended as it provides a good balance of quality and size. For high-end systems, Q6_K_L (27.26GB) offers near-perfect quality, while resource-constrained systems can effectively use IQ3 or IQ2 variants.
