NEW Qwen 2.5 Coder 32b BEST Open Source LLM?🤖 TESTED Beats GPT-4o Cursor AI Compose + Artifacts

Summary

In this video, Josh Poock explores the new Quen 2.5 Coder 32B Instruct model, an open-source LLM that claims to rival GPT-4 in coding tasks. The model supports over 40 programming languages and has been tested on a variety of tasks, including Python functions, web development, and game creation. While its performance on benchmarks is impressive, it doesn’t quite reach GPT-4 or Claude 3.5 in harder challenges. Josh provides practical testing results and highlights ways to integrate the model via Hugging Face, Cursor, and OpenRouter, encouraging viewers to test it out themselves.

Takeaways

• 😀 Quen 2.5 Coder 32B is an open-source LLM designed to rival proprietary models like GPT-4, with impressive benchmarks in code generation and reasoning.
• 💻 The model supports over 40 programming languages and scored 65.9 on Mick eval, demonstrating strong coding capabilities.
• 📏 Quen 2.5 Coder 32B has a 128k token context window, allowing it to handle large codebases and complex tasks effectively.
• 🚀 Quen 2.5 Coder 32B can be accessed via platforms like Hugging Face, Open Router, and Cursor, and can also be run locally with the right hardware.
• 📝 The model performed well on basic tasks, including generating Python functions, Bash scripts, SVGs, and even a Pong game.
• 🌐 For web development, it successfully created a responsive grid layout and a basic beauty store landing page using HTML, CSS, and JavaScript.
• 📉 Despite its strengths, the model struggled with harder coding problems, including some LeetCode challenges and complex code reasoning tasks.
• ⚙️ The model is useful for generating code, multi-file editing, and working with APIs like OpenAI's, making it a strong choice for developers seeking an open-source solution.
• 🔗 The Quen 2.5 Coder 32B model operates under the Apache 2.0 license, offering flexibility for developers to integrate and modify it.
• 💡 While it excels in open-source benchmarks, Quen 2.5 Coder 32B is not yet at the level of top-tier proprietary models like GPT-4 or Claude 3.5 in terms of complex task performance.
• 💬 The speaker encourages viewers to check out additional resources, including blog posts and further benchmarks, to dive deeper into the model's capabilities and use cases.

Q & A

What is Quen 2.5 and why is it considered significant in the field of open-source LLMs?

Quen 2.5 is an open-source large language model (LLM) that is considered significant due to its competitive performance compared to proprietary models like GPT-4. It is designed to handle complex coding tasks across various programming languages and supports a wide range of AI capabilities, making it a top contender in the open-source LLM landscape.

How does Quen 2.5's 32B model compare to GPT-4 and other models in terms of benchmarks?

According to the benchmarks mentioned, Quen 2.5's 32B model shows strong performance, rivalling GPT-4 in coding tasks. It scored highly in various evaluation tests, including human eval, mbpp, Live code, and others, indicating it performs at a competitive level or even surpasses GPT-4 in some coding benchmarks.

What are some of the key capabilities of Quen 2.5 Coder 32B Instruct model?

The Quen 2.5 Coder 32B Instruct model excels in code generation, code repair, and code reasoning. It supports over 40 programming languages and has strong general and mathematical abilities. It also features a 128k token context window length, which allows it to handle larger inputs and provide more context for tasks.

How can users access and use the Quen 2.5 Coder 32B model?

Users can access the Quen 2.5 Coder 32B model via platforms like Hugging Face, or by running it locally with tools like Olama or LLM Studio. It can also be integrated into applications like Cursor, where users can configure it by overriding API keys and model URLs.

What testing scenarios were used to evaluate the performance of the Quen 2.5 Coder 32B model?

The model was tested across multiple scenarios, including basic coding tasks like generating Python functions, creating bash scripts, and generating SVGs, as well as more complex challenges such as creating a Pong game and developing responsive web layouts. It also underwent more advanced evaluations in competitive coding environments like LeetCode.

What were some of the successes and limitations observed during the tests?

The Quen 2.5 model successfully completed basic tasks like generating Python functions and bash scripts. It performed well in medium difficulty coding tests. However, it faced challenges with more complex tasks, such as generating the correct solution for harder LeetCode problems, showing that it’s still not at the level of more advanced models like GPT-4 or Claude 3.5.

How does Quen 2.5 handle multi-file editing and complex code generation?

In scenarios requiring multi-file code generation, such as creating a modern task manager app using Next.js, Quen 2.5 performed well. It was able to generate the necessary files, although the output was sometimes basic or inefficient. Nonetheless, it demonstrated good capabilities in handling multiple files and integrating them into a cohesive application.

How does Quen 2.5 handle UI development tasks?

Quen 2.5 was able to generate the basic structure for a beauty store landing page using HTML, CSS, and JavaScript. While the design was functional, it wasn’t highly visually appealing. Similarly, for a responsive grid layout task, it successfully created the layout that adapted to different screen sizes, passing the test with flying colors.

What does the video suggest about the open-source AI model landscape in comparison to proprietary models like GPT-4?

The video suggests that while Quen 2.5 is impressive for an open-source model, it is not yet at the same level as proprietary models like GPT-4 or Claude 3.5. However, it shows promise, especially in coding and general AI tasks, and is considered one of the top-performing open-source models.

What are the potential applications of Quen 2.5 in business and development?

Quen 2.5 has the potential to be used in various business applications, especially for automating coding tasks, improving development efficiency, and generating solutions for complex programming challenges. Its ability to support multiple programming languages and perform advanced code generation tasks makes it a valuable tool for software development, web development, and even AI-powered business systems.