Qwen2-1.5B
| Property | Value |
|---|---|
| Parameter Count | 1.54B |
| License | Apache 2.0 |
| Tensor Type | BF16 |
| Architecture | Transformer with SwiGLU activation |
What is Qwen2-1.5B?
Qwen2-1.5B is part of the new series of Qwen large language models, representing a significant advancement in base language model capabilities. With 1.54 billion parameters, it's designed for multiple tasks including language understanding, generation, and coding. The model demonstrates competitive performance against both open-source and proprietary models across various benchmarks.
Implementation Details
The model is built on the Transformer architecture with several key innovations including SwiGLU activation, attention QKV bias, and group query attention. It requires transformers>=4.37.0 for proper implementation and features an improved tokenizer that's adaptive to multiple natural languages and code.
- Advanced architecture with SwiGLU activation
- Group query attention mechanism
- Improved multilingual tokenizer
- BF16 tensor type optimization
Core Capabilities
- Strong performance in MMLU (56.5%) and GSM8K (58.5%)
- Excellent multilingual capabilities with strong performance in C-Eval (70.6%)
- Code generation and mathematical reasoning
- Advanced language understanding across multiple domains
Frequently Asked Questions
Q: What makes this model unique?
Qwen2-1.5B stands out for its balanced performance across various tasks, particularly excelling in multilingual capabilities and mathematical reasoning. It achieves state-of-the-art results in several benchmarks while maintaining a relatively compact parameter count.
Q: What are the recommended use cases?
The model is primarily designed as a base language model for further fine-tuning. It's recommended for post-training applications such as SFT, RLHF, or continued pretraining rather than direct text generation tasks.
