Qwen2.5-Coder-14B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 14.7B (13.1B Non-Embedding) |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm, and QKV bias |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
| Model URL | Qwen/Qwen2.5-Coder-14B-Instruct-AWQ |
What is Qwen2.5-Coder-14B-Instruct-AWQ?
Qwen2.5-Coder-14B-Instruct-AWQ is a highly optimized code-specific language model that represents a significant advancement in the Qwen series. This 4-bit quantized version maintains the powerful capabilities of the original model while reducing its computational footprint. Trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, it's specifically designed for code generation, reasoning, and fixing tasks.
Implementation Details
The model utilizes advanced architectural elements including 48 layers with 40 attention heads for queries and 8 for key/values (GQA). It implements RoPE positional embeddings, SwiGLU activations, and RMSNorm, with support for extensive context windows up to 128K tokens through YaRN scaling.
- Full 131,072 token context length support
- AWQ 4-bit quantization for efficient deployment
- Comprehensive code generation and reasoning capabilities
- Built on transformers architecture with advanced optimizations
Core Capabilities
- Advanced code generation and completion
- Code reasoning and debugging
- Mathematical problem-solving
- Long-context processing with YaRN support
- Efficient deployment through 4-bit quantization
Frequently Asked Questions
Q: What makes this model unique?
This model combines state-of-the-art code generation capabilities with efficient 4-bit quantization, making it both powerful and deployable. Its extensive context length of 128K tokens and specialized training on code-related tasks sets it apart from general-purpose language models.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and software development tasks. It's particularly suitable for environments where both high-quality code generation and efficient resource usage are priorities. The model can handle everything from algorithm implementation to code review and optimization.
