Qwen2.5.1-Coder-7B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Base Model | Qwen/Qwen2.5-Coder-7B-Instruct |
| Quantization | Multiple options (2.7GB-15GB) |
What is Qwen2.5.1-Coder-7B-Instruct-GGUF?
Qwen2.5.1-Coder-7B-Instruct-GGUF is a specialized language model optimized for coding tasks, offering various quantization options to balance performance and resource requirements. It's based on the Qwen architecture and has been converted to the efficient GGUF format for broader compatibility and deployment options.
Implementation Details
The model comes in multiple quantization variants, from the full F16 weights (15.24GB) down to highly compressed versions (2.78GB), each optimized for different use cases and hardware configurations. It uses imatrix quantization with a specialized dataset for maintaining code generation quality while reducing model size.
- Supports multiple quantization levels (Q8_0 to IQ2_M)
- Special optimizations for ARM inference
- Customized prompt format with system, user, and assistant roles
- Optimized embedding weights for improved performance
Core Capabilities
- Code generation and completion
- Programming language understanding
- Technical documentation generation
- Code explanation and analysis
- Flexible deployment options across different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
The model combines the powerful Qwen architecture with specialized coding capabilities and offers an extensive range of quantization options, making it highly adaptable to different hardware constraints while maintaining performance.
Q: What are the recommended use cases?
For most users, the Q4_K_M (4.68GB) variant is recommended as it provides a good balance between quality and size. For high-performance systems, Q6_K_L offers near-perfect quality, while resource-constrained systems can use IQ3_XS or lower quantizations.
