Certainly! Here is the translation of the "Qwen2.5 Technical Report" into English: --- **Qwen2.5 Technical Report** Qwen2.5 is the latest iteration of the Qwen series, a large language model developed by Alibaba Cloud. This technical report provides an in-depth overview of the advancements and features introduced in Qwen2.5, highlighting its capabilities in natural language processing (NLP) and its potential applications in various fields. ### 1. Introduction Qwen2.5 builds upon the success of its predecessors, Qwen1.0 and Qwen2.0, by incorporating state-of-the-art techniques and a significantly larger training dataset. The model aims to enhance performance in tasks such as text generation, question answering, and dialogue systems, while also improving robustness and reducing biases. ### 2. Model Architecture The architecture of Qwen2.5 is based on the Transformer model, which has proven to be highly effective in NLP tasks. Key enhancements include: - **Increased Model Size**: Qwen2.5 has a larger number of parameters compared to previous versions, allowing it to capture more complex patterns in data. - **Advanced Attention Mechanisms**: The model employs advanced attention mechanisms to improve context understanding and coherence in generated text. - **Efficient Training Techniques**: New training techniques have been implemented to optimize the training process, making it faster and more resource-efficient. ### 3. Training Data Qwen2.5 was trained on a diverse and extensive dataset that includes: - **Web Text**: A vast collection of web pages, articles, and other textual content. - **Books**: A wide range of literary works, including fiction and non-fiction. - **News Articles**: Up-to-date news articles from various sources. - **Scientific Papers**: Research papers from multiple scientific disciplines. - **Multilingual Data**: Text data from multiple languages to support cross-lingual tasks. ### 4. Performance Evaluation To evaluate the performance of Qwen2.5, several benchmark tests were conducted: - **Text Generation**: Qwen2.5 demonstrated superior text generation capabilities, producing coherent and contextually relevant content. - **Question Answering**: The model showed significant improvements in accuracy for both closed-book and open-book question answering tasks. - **Dialogue Systems**: Qwen2.5 excelled in maintaining natural and engaging conversations with users. ### 5. Applications Qwen2.5 has a wide range of potential applications across different industries: - **Content Creation**: Generating high-quality articles, reports, and creative writing. - **Customer Service**: Enhancing chatbot interactions for better customer support. - **Research Assistance**: Assisting researchers by summarizing papers and generating hypotheses. - **Educational Tools**: Developing interactive learning materials and tutoring systems. ### 6. Ethical Considerations Alibaba Cloud is committed to ensuring that Qwen2.5 is used responsibly and ethically: - **Bias Mitigation**: Efforts have been made to reduce biases in the model's outputs through careful data selection and post-processing techniques. - **Transparency**: Detailed documentation is provided to help users understand how the model works and its limitations. - **User Privacy**: Measures are in place to protect user data and ensure privacy during interactions with the model. ### 7. Future Work Future developments for Qwen2.5 will focus on: - **Further Enhancements**: Continuously improving the model's performance through research and development. - **Multimodal Capabilities**: Exploring integration with other modalities such as images and videos to expand its application areas. - **Scalability**: Ensuring that the model can be scaled efficiently to handle larger datasets and more complex tasks. ### 8. Conclusion Qwen2.5 represents a significant step forward in the field of large language models, offering enhanced capabilities and robust performance across a variety of NLP tasks. Its potential applications are vast, making it a valuable tool for businesses, researchers, and developers alike. --- If you need any further details or specific sections translated differently, please let me know!
In this report, we introduce Qwen2.5, a comprehensive series of large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has been significantly improved during both the pre-training and post-training stages. In terms of pre-training, we have scaled the high-quality pre-training datasets from the previous 7 trillion tokens to 18 trillion tokens. This provides a strong foundation for common sense, expert knowledge, and reasoning capabilities. In terms of post-training, we implement intricate supervised finetuning with over 1 million samples, as well as multistage reinforcement learning. Post-training techniques enhance human preference, and notably improve long text generation, structural data analysis, and instruction following. To handle diverse and varied use cases effectively, we present Qwen2.5 LLM series in rich sizes. Open-weight offerings include base and instruction-tuned models, with quantized versions available. In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2.5-Turbo and Qwen2.5-Plus, both available from Alibaba Cloud Model Studio. Qwen2.5 has demonstrated top-tier performance on a wide range of benchmarks evaluating language understanding, reasoning, mathematics, coding, human preference alignment, etc. Specifically, the open-weight flagship Qwen2.5-72B-Instruct outperforms a number of open and proprietary models and demonstrates competitive performance to the state-of-the-art open-weight model, Llama-3-405B-Instruct, which is around 5 times larger. Qwen2.5-Turbo and Qwen2.5-Plus offer superior cost-effectiveness while performing competitively against GPT-4o-mini and GPT-4o respectively. Additionally, as the foundation, Qwen2.5 models have been instrumental in training specialized models such as Qwen2.5-Math, Qwen2.5-Coder, QwQ, and multimodal models.