Qwen2.5-Coder-7B-Instruct-GPTQ-Int8
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| Model Type | Causal Language Model |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 8-bit |
| Paper | Qwen2.5-Coder Technical Report |
What is Qwen2.5-Coder-7B-Instruct-GPTQ-Int8?
Qwen2.5-Coder-7B-Instruct-GPTQ-Int8 is an advanced code-specific language model that represents the latest evolution in the Qwen series. This 8-bit quantized version maintains high performance while reducing computational requirements, featuring 28 layers and an impressive 131,072 token context length.
Implementation Details
The model architecture combines several cutting-edge components including RoPE (Rotary Position Embedding), SwiGLU activation, RMSNorm layer normalization, and specialized attention mechanisms with 28 heads for queries and 4 for key-values. The model employs GPTQ 8-bit quantization to optimize deployment efficiency while maintaining performance.
- Advanced architecture with 28 attention layers
- Grouped-Query Attention (GQA) implementation
- YaRN-enabled long context processing
- 8-bit quantization for efficient deployment
Core Capabilities
- Superior code generation and reasoning abilities
- Extended context length support up to 128K tokens
- Enhanced mathematics and general competencies
- Optimized for real-world code agent applications
- Efficient handling of long-form programming tasks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of efficient 8-bit quantization, extensive context length, and specialized code generation capabilities. It's built on 5.5 trillion training tokens including source code and text-code grounding data, making it particularly effective for programming tasks.
Q: What are the recommended use cases?
This model is ideal for code generation, code reasoning, and fixing tasks. It's particularly well-suited for developing code agents, handling long programming contexts, and supporting mathematical computations. The model can effectively process inputs up to 128K tokens, making it valuable for large-scale code analysis and generation tasks.
