Qwen2.5-Coder-7B-Instruct-GPTQ-Int4
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| Model Type | Causal Language Model (Code-Specialized) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 4-bit |
| Paper | Qwen2.5-Coder Technical Report |
What is Qwen2.5-Coder-7B-Instruct-GPTQ-Int4?
Qwen2.5-Coder-7B-Instruct-GPTQ-Int4 is a state-of-the-art code-specialized language model that represents a significant advancement in AI-powered coding assistance. Built on the foundation of Qwen2.5, this model has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, making it particularly adept at code generation, reasoning, and fixing tasks.
Implementation Details
The model features a sophisticated architecture with 28 layers and an innovative attention mechanism utilizing 28 heads for queries and 4 heads for keys/values. Its 4-bit quantization through GPTQ enables efficient deployment while maintaining performance. The model supports an impressive context length of 131,072 tokens through YaRN scaling technology.
- Advanced transformer architecture with RoPE, SwiGLU, and RMSNorm
- Grouped-Query Attention (GQA) implementation
- GPTQ 4-bit quantization for efficient deployment
- YaRN-based context length scaling
Core Capabilities
- Superior code generation and completion
- Advanced code reasoning and problem-solving
- Efficient code fixing and debugging
- Long-context processing up to 128K tokens
- Maintained strength in mathematics and general tasks
Frequently Asked Questions
Q: What makes this model unique?
The model combines high-performance code generation capabilities with efficient 4-bit quantization, making it both powerful and deployable in resource-constrained environments. Its extensive context length and specialized training on code-related tasks set it apart from general-purpose language models.
Q: What are the recommended use cases?
This model excels in software development tasks, including code generation, debugging, and technical documentation. It's particularly well-suited for code agents, development environments, and automated coding assistance tools. The long context length makes it ideal for handling large codebases and complex programming scenarios.
