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Qwen3-14B

Parameters

14B

Context Length

131.072K

Modality

Text

Architecture

Dense

License

Apache 2.0

Release Date

29 Apr 2025

Knowledge Cutoff

-

Technical Specifications

Attention Structure

Grouped-Query Attention

Hidden Dimension Size

-

Number of Layers

48

Attention Heads

80

Key-Value Heads

8

Activation Function

-

Normalization

Layer Normalization

Position Embedding

ROPE

System Requirements

VRAM requirements for different quantization methods and context sizes

Qwen3-14B

Qwen3-14B is a causal language model developed by the Qwen team at Alibaba Cloud, belonging to the Qwen3 series. This model is engineered with a dense architecture, encompassing 14.8 billion parameters. A core aspect of its design is the capacity for dynamic mode switching between a "thinking" mode for intricate analytical tasks and a "non-thinking" mode for efficient general-purpose dialogue. This dual operational capability aims to optimize performance and utility across a broad spectrum of natural language processing applications.

From an architectural standpoint, Qwen3-14B incorporates a Grouped Query Attention (GQA) mechanism, configured with 40 query heads and 8 key/value heads, which contributes to its computational efficiency. The model is structured with 40 layers. It supports a native context length of 32,768 tokens, which can be expanded to 131,072 tokens through the application of the YaRN (Yet another RoPE N) technique for Rotary Position Embeddings. Further architectural refinements include the implementation of qk layernorm, which is integrated across all Qwen3 models to enhance training stability and overall performance.

In terms of its operational characteristics, the thinking mode of Qwen3-14B demonstrates enhanced reasoning capabilities, particularly in domains such as mathematics, code generation, and complex logical inference. Conversely, the non-thinking mode is optimized for tasks requiring general dialogue, instruction following, and creative content generation. The model supports over 100 languages and dialects, showcasing robust multilingual processing capabilities. Its design also facilitates integration with external tools, endowing it with agentic functionalities for addressing complex, multi-step problems. These features position Qwen3-14B as a versatile asset for applications ranging from advanced AI assistants requiring analytical depth to interactive conversational systems.

About Qwen 3

The Alibaba Qwen 3 model family comprises dense and Mixture-of-Experts (MoE) architectures, with parameter counts from 0.6B to 235B. Key innovations include a hybrid reasoning system, offering 'thinking' and 'non-thinking' modes for adaptive processing, and support for extensive context windows, enhancing efficiency and scalability.


Other Qwen 3 Models

Evaluation Benchmarks

Ranking is for Local LLMs.

Rank

#11

BenchmarkScoreRank

0.68

4

0.74

7

0.73

9

0.58

12

Rankings

Overall Rank

#11

Coding Rank

#20

GPU Requirements

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Context Size: 1,024 tokens

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