| Property | Value |
|---|---|
| Parameter Count | 1.54B |
| Context Length | 32,768 tokens |
| License | Apache 2.0 |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Research Paper | arXiv:2409.12186 |
What is Qwen2.5-Coder-1.5B-Instruct?
Qwen2.5-Coder-1.5B-Instruct is part of the latest series of Code-Specific Qwen large language models, specifically designed for code generation and reasoning tasks. It represents a significant advancement in the Qwen family, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model architecture employs sophisticated components including 28 layers and a hybrid attention system with 12 heads for queries and 2 for key-values. It utilizes BF16 precision and implements advanced features like RoPE positional embeddings and SwiGLU activations.
- Full 32K token context window
- Efficient architecture with 1.31B non-embedding parameters
- Optimized for both code generation and general tasks
- Implements Group Query Attention (GQA)
Core Capabilities
- Advanced code generation and completion
- Code reasoning and problem-solving
- Bug identification and fixing
- Mathematical computation support
- Text-code grounding tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that balances size and performance, offering professional-grade coding capabilities in a relatively compact 1.5B parameter package. It's particularly notable for its extensive 32K context window and specialized training on code-specific tasks.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and technical documentation tasks. It's particularly well-suited for developers needing AI assistance in coding projects, code review processes, and educational programming contexts.