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Qwen2-7B-Instruct

Qwen2-7B-Instruct

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Model parameter quantity
7B
Model parameter quantity
Affiliated organization
Alibaba
Affiliated organization
Open Source
License Type
Release time
June 6, 2024
Release time

Model Introduction
Qwen2 is the new series of Qwen large language models.
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Language comprehension ability Language comprehension ability
Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
4.3
Knowledge coverage scope Knowledge coverage scope
Knowledge coverage scope
Has significant knowledge blind spots, often showing factual errors and repeating outdated information.
6.4
Reasoning ability Reasoning ability
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
3.6
Related model
Qwen3-235B-A22B-Instruct-2507 Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
Qwen3-235B-A22B-Thinking-2507 Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
Qwen2.5-7B-Instruct Like Qwen2, the Qwen2.5 language models support up to 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
Qwen3-32B (Thinking) Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
Qwen1.5-72B-Chat Qwen1.5 is the beta version of Qwen2, maintaining its architecture as a decoder-only transformer model with SwiGLU activation, RoPE, and multi-head attention mechanisms. It offers nine model sizes and has enhanced multilingual and chat model capabilities, supporting a context length of 32,768 tokens. All models have enabled system prompts for roleplaying, and the code supports native implementation in transformers.
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