Qwen 2 LLM Unleashed: The SMALLEST Model That Can Outcode Llama 3?

The release of Quen 2, developed by Chinese AI company Cing, introduces models ranging from 0.5b to 272b with a focus on coding and math performance, showcasing improved logical reasoning capabilities through fine-tuning on relevant datasets. Quen 2, particularly the 272b model, demonstrates significant performance improvements in math and coding tasks, prioritizing long context understanding and surpassing previous models like RAG, while generating discussions about the trade-off between performance and safety in AI development.

The release of Quen 2 has been generating a lot of excitement as it competes with Llama 3 and other latest models in the AI field. It sheds light on how Chinese AI companies like Cing approach AI differently from Western counterparts, focusing on performance rather than safety. Quen 2 offers models ranging from 0.5b to 272b, trained in multiple languages, with a focus on coding and math performance. The model boasts state-of-the-art performance in various benchmarks and extends its context length to 128,000 tokens. By fine-tuning on datasets related to coding and language, Quen 2 showcases improved logical reasoning capabilities.

The Quen 272b model demonstrates a clear margin of improvement in performance across various benchmarks, particularly excelling in math and coding tasks. The developers highlight the use of rejection sampling and other approaches to enhance the model’s performance. Through extensive blog posts, they delve into technical details of achieving these advancements. Quen 2 prioritizes long context understanding, surpassing the previous popularity of RAG models in mid-2023.

The model showcases its capabilities through tasks like the mirror test and coding questions. It demonstrates understanding of spatial reasoning in estimating sphere sizes and coding tasks like setting up a landing page using React. The model’s ability to handle common coding tasks efficiently and accurately is evident. However, it may lack guidance for beginners in structuring code.

There are observations regarding the model’s quantization and performance, with some quirks noted during further quantization processes. The aggressive use of GQA in the 7B version may limit post-quantization capabilities. Additionally, upcoming work based on Quen 272b, such as Dolphin 292, shows promising performance gains. The discussion around whether China’s approach to optimizing performance compromises safety compared to the West’s open-source AI models remains a topic of debate. Despite differing strategies, the advancements in AI models like Quen 2 continue to shape the AI landscape.