Technical Specifications

Qwen3-4B

Qwen3-4B is a 4-billion parameter dense causal language model developed by Alibaba, belonging to the third generation of the Qwen series. A fundamental innovation in this model is its unified architecture that supports dual-mode operation, allowing for dynamic switching between 'thinking' and 'non-thinking' states. In the thinking mode, the model performs extensive, multi-step logical reasoning similar to chain-of-thought processing, making it effective for complex mathematical problems and intricate code generation. Conversely, the non-thinking mode is optimized for low-latency, direct responses in general conversational contexts, providing an efficient alternative for tasks where depth of reasoning is secondary to speed.

Technically, the model is built on a transformer architecture with 36 layers and 4.0 billion total parameters. It utilizes Grouped Query Attention (GQA) with 32 attention heads for queries and 8 key-value heads, ensuring high computational throughput during inference. The model employs Rotary Position Embeddings (RoPE) and is natively trained on a 32,768-token context window, which can be extended up to 131,072 tokens using YaRN scaling. This architectural foundation is further refined through a three-stage pre-training pipeline involving 36 trillion tokens across 119 languages, prioritizing a mix of high-quality STEM, coding, and multilingual data to ensure broad-spectrum proficiency.

Qwen3-4B is designed for versatility in deployment, particularly in environments requiring sophisticated reasoning within a compact parameter footprint. Its native support for thinking modes allows it to function as a reasoning engine for complex instruction following and agentic workflows without requiring a separate specialized model. The integration of SwiGLU activations and RMSNorm ensures stable training dynamics, while the inclusion of 'tied embeddings' specifically in the smaller variants like the 4B model helps optimize memory usage. It is highly effective for cross-lingual tasks, tool-based interactions, and structured output generation across a wide variety of domains.

No evaluation benchmarks for Qwen3-4B available.