Qwen2.5-1.5B
| Property | Value |
|---|---|
| Parameter Count | 1.54B (1.31B Non-Embedding) |
| Model Type | Causal Language Model |
| License | Apache-2.0 |
| Context Length | 32,768 tokens |
| Paper | Research Paper |
What is Qwen2.5-1.5B?
Qwen2.5-1.5B is part of the latest Qwen series of large language models, representing a significant advancement in base language model capabilities. This 1.54B parameter model is designed for pretraining and serves as a foundation for various downstream tasks through fine-tuning.
Implementation Details
The model utilizes a sophisticated architecture incorporating transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It features 28 layers with 12 attention heads for queries and 2 for key-values, implementing Grouped Query Attention (GQA) for efficient processing.
- Full 32,768 token context length support
- BF16 tensor type for optimal performance
- Integrated word embeddings for improved efficiency
- Advanced architecture with RoPE and SwiGLU activations
Core Capabilities
- Enhanced knowledge representation and processing
- Improved coding and mathematical capabilities
- Support for 29+ languages including major world languages
- Structured data understanding and JSON output generation
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture combining GQA attention mechanisms with extensive multilingual support and significant improvements in structured data handling. It's specifically designed as a base model for further fine-tuning.
Q: What are the recommended use cases?
While not recommended for direct conversational use, this base model is ideal for post-training applications including SFT, RLHF, and continued pretraining. It's particularly well-suited for tasks requiring strong foundational language understanding and structured output generation.
