Model Overview Card for Qwen2 7B Instruct

Basic Information

• Model Name: Qwen2-7B-Instruct
• Developer/Creator: Qwen (Alibaba Group)
• Release Date: June 7, 2024
• Version: Not specified
• Model Type: Large Language Model (LLM)

Description

Overview

Qwen2-7B-Instruct is an instruction-tuned large language model with 7.07 billion parameters, part of the Qwen2 series. It demonstrates state-of-the-art performance across various benchmarks and excels in coding and mathematical tasks

Key Features

• Extended context length support up to 128K tokens
• Significantly improved performance in coding and mathematics
• Trained on data in 27 additional languages besides English and Chinese
• Utilizes Group Query Attention (GQA) for faster speed and less memory usage
• State-of-the-art performance in numerous benchmark evaluations

Intended Use

The model is designed for a wide range of natural language processing tasks, including:

• Text generation
• Language understanding
• Coding tasks
• Mathematical problem-solving
• Multilingual applications

Language Support

Qwen2-7B-Instruct supports multiple languages, including:

• English and Chinese (primary)
• 27 additional languages from Western Europe, Eastern & Central Europe, Middle East, Eastern Asia, South-Eastern Asia, and Southern Asia

Technical Details

Architecture

• Based on the Transformer architecture
• Implements Group Query Attention (GQA)
• Does not use tied embeddings

Training Data

The model has been trained on data from at least 29 languages, significantly enhancing its multilingual capabilities.

Data Source and Size

The exact size of the training data is not specified in the available information.

Knowledge Cutoff

The knowledge cutoff date for Qwen2-7B-Instruct is not explicitly stated in the provided information.

Diversity and Bias

The model has been trained on diverse datasets spanning multiple languages and regions, which may contribute to reduced bias. However, specific information about bias evaluation is not provided in the available sources.

Performance Metrics

Qwen2-7B-Instruct has demonstrated strong performance across various benchmarks:

• Outperforms many open-source models in language understanding and generation tasks
• Excels in coding tasks and metrics related to Chinese language proficiency
• Shows competitive performance against proprietary models on certain benchmarks

Comparison to Other Models

Accuracy

Qwen2-7B-Instruct shows superior performance compared to similar-sized models across various benchmarks, particularly in coding and Chinese-related metrics.

Speed

Specific information about inference speed is not provided, but the use of GQA suggests improved speed compared to models without this feature.

Robustness

The model demonstrates strong generalization capabilities across different topics and languages, as evidenced by its performance on diverse benchmarks and multilingual support.

Usage

Code Samples

Ethical Guidelines

While specific ethical guidelines are not provided, users should be aware of potential biases and limitations inherent in large language models. The model has been designed with safety considerations in mind.

Licensing

Qwen2-7B-Instruct is released under the Apache 2.0 license, allowing for both research and commercial use.

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