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Qwen 2.5 7B Instruct Turbo excels in coding and instruction following.
Basic Information
Description
Overview:
The Qwen 2.5 7B Instruct model is a cutting-edge large language model designed to understand and generate text based on specific instructions. It excels in various tasks, including coding, mathematical problem-solving, and generating structured outputs.
Key Features:
Intended Use:
This model is intended for software developers, researchers, and businesses looking to leverage advanced natural language processing capabilities in applications such as:
Language Support:
Qwen 2.5 supports multiple languages, making it versatile for global applications.
Architecture:
Qwen 2.5 utilizes a Transformer architecture with enhancements like RoPE (Rotary Positional Embedding), SwiGLU activation functions, RMSNorm normalization, and Attention QKV bias. It consists of 28 layers and 28 attention heads for query processing.
Training Data:
The model was trained on an extensive dataset comprising over 18 trillion tokens, sourced from diverse domains such as books, websites, and programming repositories. This broad dataset enhances its understanding of various topics.
Data Source and Size:
The training data includes a rich mix of text types and programming languages, ensuring the model's robustness and adaptability across different contexts.
Knowledge Cutoff:
The model's knowledge is current as of October 2024.
Diversity and Bias:
Efforts were made to ensure the training data is diverse to reduce biases. However, like all AI models, it may still reflect some inherent biases present in the data.
Key performance metrics for Qwen 2.5 7B Instruct include:
Code Samples:
Ethical Guidelines
The development of Qwen 2.5 adheres to ethical AI principles, emphasizing transparency, fairness, and accountability in its applications. Users are encouraged to consider these guidelines when deploying the model for various tasks.
LThe Qwen 2.5 models are available under the Apache 2.0 License for commercial and non-commercial use.
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