Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Qwen3-1.7B

Qwen3-1.7B is a dense causal language model developed by Alibaba's Qwen Team, introduced as part of the Qwen3 series on April 29, 2025. This model is designed for general-purpose language tasks and is characterized by its compact 1.7 billion parameter count. Its architecture is optimized for efficient operation across various hardware configurations, including environments with limited resources and edge devices. The model supports a context length of 32,768 tokens, enabling it to process extensive documents and conversations.

A distinguishing architectural feature within the Qwen3 series, including the 1.7B variant, is its dual operational modes: "Thinking Mode" and "Non-Thinking Mode." The Thinking Mode facilitates complex logical reasoning, such as mathematical problem-solving and code generation, through a step-by-step reasoning process. In contrast, the Non-Thinking Mode provides rapid, direct responses suitable for general conversational applications. This hybrid approach enables dynamic switching between modes, optimizing performance based on task complexity and efficiency requirements.

The model's architecture consists of 28 transformer layers, employing Grouped Query Attention (GQA) with 16 query heads and 8 key-value heads. It integrates Rotary Positional Embeddings (RoPE), specifically enhanced with ABF-RoPE, to maintain positional information accuracy across its extended context length. Further architectural refinements include the implementation of qk layernorm and RMSNorm with pre-normalization for stable training. Qwen3-1.7B demonstrates robust multilingual support, processing over 100 languages and dialects, and features advanced agent capabilities for tool integration.

No evaluation benchmarks for Qwen3-1.7B available.