Replete-LLM-V2.5-Qwen-7b-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Base Model | Rombos-LLM-V2.5-Qwen-7b |
| Quantization | Multiple GGUF variants |
What is Replete-LLM-V2.5-Qwen-7b-GGUF?
This is a comprehensive collection of GGUF quantized versions of the Qwen 7B model, optimized using llama.cpp's imatrix quantization. The model provides various quantization options ranging from 15.24GB (F16) down to 2.78GB (IQ2_M), allowing users to choose the best balance between model size and performance for their specific hardware setup.
Implementation Details
The model uses a specific prompt format with system, user, and assistant markers. It offers multiple quantization variants optimized for different hardware configurations, including special optimizations for ARM processors.
- Multiple quantization levels from F16 to IQ2
- Special embed/output weight handling in XL/L variants
- ARM-specific optimizations in Q4_0_X_X variants
- imatrix calibration for improved performance
Core Capabilities
- Text generation and conversation
- Efficient inference on various hardware configurations
- Optimized performance-to-size ratios
- Support for system-level instructions
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options and specific optimizations for different hardware configurations. The imatrix quantization method and special handling of embedding weights in certain variants provide enhanced performance while maintaining quality.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (4.68GB) is recommended as a balanced option. Users with limited RAM should consider the Q3 or IQ3 variants, while those prioritizing quality should opt for Q6_K_L or Q5_K_L variants. ARM users should specifically consider the Q4_0_X_X variants for optimal performance.
