WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache-2.0 |
| Base Model | WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B |
| Quantizer | bartowski |
What is WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B-GGUF?
This is a comprehensive quantized version of the WhiteRabbitNeo Coder model, specifically optimized for efficient deployment across various hardware configurations. The model offers multiple quantization levels, from high-precision 16-bit floating point (15.24GB) down to highly compressed 2-bit versions (2.78GB), allowing users to balance performance with hardware constraints.
Implementation Details
The model utilizes llama.cpp's advanced quantization techniques with imatrix calibration, offering various compression formats including K-quants and I-quants. Each variant is carefully optimized using specialized calibration datasets, ensuring optimal performance even at higher compression rates.
- Multiple quantization options ranging from Q8_0 to IQ2_M
- Special optimizations for ARM inference with Q4_0_X_X variants
- Enhanced embed/output weight handling in XL and L variants
- Standardized prompt format using im_start/im_end tokens
Core Capabilities
- Code generation and completion
- Technical conversation and assistance
- Efficient deployment across various hardware configurations
- Memory-optimized inference with minimal quality loss
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its extensive range of quantization options, allowing deployment on various hardware configurations while maintaining performance. It's specifically optimized for coding tasks and includes special ARM-optimized variants.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (4.68GB) offers the best balance of quality and size. For high-end systems, Q6_K_L (6.52GB) provides near-perfect quality, while resource-constrained systems can utilize IQ3_XS (3.35GB) for reasonable performance.
