Qwen2.5-Coder-32B-Instruct-abliterated-GGUF
| Property | Value |
|---|---|
| Parameter Count | 32.8B |
| License | Apache 2.0 |
| Author | bartowski |
| Base Model | Qwen2.5-Coder-32B-Instruct-abliterated |
What is Qwen2.5-Coder-32B-Instruct-abliterated-GGUF?
This is a comprehensive collection of GGUF quantizations of the Qwen2.5-Coder model, specifically designed for code generation and chat applications. The model offers various quantization levels to balance performance and resource requirements, ranging from lightweight 9.96GB versions to full 65.54GB implementations.
Implementation Details
The model uses llama.cpp for quantization and features multiple compression formats including Q8_0, Q6_K, Q5_K, and innovative IQ formats. Each quantization level is carefully calibrated using a specific dataset to maintain optimal performance while reducing size.
- Supports multiple quantization types from high-quality Q8_0 to resource-efficient IQ2_XS
- Implements special optimizations for ARM inference
- Uses advanced embedding weight handling in certain variants
- Features a specific prompt format for optimal interaction
Core Capabilities
- Code generation and completion
- Chat-based interactions
- Reduced censorship compared to base models
- Flexible deployment options across different hardware configurations
- Optimized performance on both CPU and GPU systems
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extensive range of quantization options, making it highly adaptable to different hardware constraints while maintaining performance. It also features specific optimizations for ARM processors and reduced censorship compared to standard models.
Q: What are the recommended use cases?
The model is ideal for code generation tasks and chat applications. For users with high-end hardware, the Q6_K_L or Q5_K_M variants are recommended. For those with limited resources, the IQ4_XS or Q4_K_M variants offer a good balance of performance and efficiency.
