open-r1_OlympicCoder-7B-GGUF
| Property | Value |
|---|---|
| Original Model | OlympicCoder-7B |
| Quantization | Multiple GGUF formats |
| Size Range | 2.78GB - 15.24GB |
| Author | bartowski |
| Source | Hugging Face |
What is open-r1_OlympicCoder-7B-GGUF?
This is a comprehensive collection of GGUF quantized versions of the OlympicCoder-7B model, optimized using llama.cpp's imatrix quantization technique. The collection offers various compression levels to accommodate different hardware capabilities and use-case requirements, ranging from full BF16 precision to highly compressed formats.
Implementation Details
The model uses a specific prompt format with system, user, and assistant components. It's implemented using llama.cpp release b4867 for quantization, with special attention to embed/output weight handling in certain variants.
- Offers multiple quantization types from BF16 to IQ2_M
- Includes specialized versions with Q8_0 embeddings for better quality
- Supports online repacking for ARM and AVX CPU inference
- Compatible with LM Studio and other llama.cpp-based projects
Core Capabilities
- Flexible deployment options across different hardware configurations
- Quality-size tradeoff options from 15.24GB (BF16) to 2.78GB (IQ2_M)
- Optimized performance for various CPU architectures
- Special quantization options for embed and output weights
Frequently Asked Questions
Q: What makes this model unique?
The model offers an extensive range of quantization options, allowing users to choose the optimal balance between model size, quality, and performance for their specific hardware setup. It includes innovative approaches like online repacking and specialized embedding quantization.
Q: What are the recommended use cases?
For high-performance systems, the Q6_K_L or Q5_K_M variants are recommended. For systems with limited RAM, the Q4_K_M offers a good balance. For minimal resource requirements, the IQ3_XXS or IQ2_M variants provide surprisingly usable performance at smaller sizes.
