Building RAG With Qwen 2
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- Опубликовано: 2 окт 2024
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The Qwen 2 model series from Alibaba Cloud represents a significant advancement in the field of large language models (LLMs). Built on the robust Transformer architecture, Qwen 2 models incorporate advanced techniques such as SwiGLU activation, attention QKV bias, and group query attention, enhancing their capability to handle diverse linguistic and contextual nuances. The Mixture of Experts (MoE) architecture in some variants of Qwen 2 further optimizes performance by activating only a subset of parameters during inference, thereby improving efficiency and reducing computational costs.
Qwen 2 models excel in various benchmarks, outperforming baseline models of similar sizes in tasks like natural language understanding, mathematical problem-solving, and coding. Notably, the Qwen-72B model surpasses the performance of LLaMA2-70B and even outperforms GPT-3.5 on several key tasks. This high level of performance is complemented by the models' scalability, with sizes ranging from 0.5B to 72B parameters, making them suitable for a wide range of applications.
One of the standout features of Qwen 2 is its multilingual support, thanks to an improved tokenizer that adapts to multiple natural languages and coding contexts. Additionally, instruction-tuned models within the Qwen 2 series are fine-tuned to follow human instructions accurately, enhancing their utility in applications requiring precise and context-aware responses.
Qwen 2 is also open-source, encouraging community collaboration and innovation. Comprehensive documentation and support provided by the Qwen team ensure that developers and researchers can effectively implement and utilize these models. With its combination of advanced technology, high performance, and community-driven development, Qwen 2 is poised to be a valuable tool for AI-driven applications across various industries.
Please make more RAG applications with advance techniques with larger context model
Hi, advanced rag videos are in the works. Coming soon
New subscriber here, can you showcase multi-language LLMs (like you are doing here with QWEN2)... since most LLMs are trained on English datasets... I recently tried QWEN2, and it seems to work well with Arabic. Also, I am looking into the Cohere Aya23 8 billion model, which works well with Arabic... and eagerly anticipate LLAMA3 for multiple languages... I appreciate your great content.
Appreciate your input on qwen 2 on arabic. didn't know it could perform that well
Why would I use this if I can just copy the link, insert it into ChatGpt and ask the same question?
You're probably right. This is just a toy example. What if you want to use various complex data sources and care about data security, would you still use chat gpt....