Qwen2.5-14B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 14.8B parameters |
| License | Apache 2.0 |
| Author | bartowski |
| Base Model | Qwen/Qwen2.5-14B-Instruct |
What is Qwen2.5-14B-Instruct-GGUF?
Qwen2.5-14B-Instruct-GGUF is a comprehensive collection of quantized versions of the Qwen2.5-14B-Instruct model, optimized using llama.cpp's latest quantization techniques. This model suite provides various compression levels to accommodate different hardware configurations while maintaining performance.
Implementation Details
The model offers multiple quantization formats ranging from 5.36GB to 29.55GB, all created using the imatrix option. Each variant is optimized for specific use cases, with some versions specifically tailored for ARM inference and others designed for maximum quality retention.
- Supports multiple quantization types (Q8_0, Q6_K, Q5_K, Q4_K, Q3_K, IQ4, IQ3, IQ2)
- Uses specialized formats for embed/output weights in certain variants
- Implements efficient ARM-optimized versions with Q4_0_X_X variants
- Features context length optimization and updated tokenizer
Core Capabilities
- Text generation and chat functionality
- Supports both English language processing
- Optimized for various hardware configurations
- Efficient inference with reduced memory footprint
- Maintains quality through strategic quantization approaches
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware constraints. It includes cutting-edge I-quant formats and specialized ARM optimizations.
Q: What are the recommended use cases?
For users with limited VRAM, the Q4_K_M variant (8.99GB) is recommended as a balanced option. Those requiring maximum quality should consider Q6_K_L (12.50GB), while users with severe resource constraints might opt for IQ2_M (5.36GB) which remains surprisingly usable despite its small size.
