Qwen2.5-14B Uncensored Instruct GGUF
| Property | Value |
|---|---|
| Parameter Count | 14.8B |
| License | Apache 2.0 |
| Format | GGUF (Multiple quantizations) |
| Language | English |
What is Qwen2.5-14B_Uncensored_Instruct-GGUF?
This is a comprehensive collection of GGUF quantized versions of the Qwen2.5-14B Uncensored Instruct model, specifically optimized for various hardware configurations and memory constraints. The model offers multiple quantization options ranging from 5.36GB to 29.55GB, allowing users to balance between performance and resource usage.
Implementation Details
The model utilizes llama.cpp's latest quantization techniques with imatrix calibration, offering both K-quants and I-quants for different use cases. It follows a specific prompt format using im_start and im_end tokens for system, user, and assistant interactions.
- Multiple quantization options (Q2 to Q8_0)
- Special optimizations for ARM chips
- Enhanced embed/output weight configurations
- Compatibility with platforms like LM Studio
Core Capabilities
- Text generation and conversation
- Flexible deployment options for various hardware
- Memory-efficient inference with minimal quality loss
- Optimized performance on different architectures (CPU, NVIDIA, AMD)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive range of quantization options and specific optimizations for different hardware architectures, including special ARM-optimized versions and innovative I-quant implementations for better performance at lower sizes.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L or Q5_K_M variants. For balanced performance, Q4_K_M is recommended. For limited RAM scenarios, IQ3_M or Q3_K_M provide decent performance while being very resource-efficient.
