Qwen2.5-Max just dropped - let's have a look, shall we?

Quen 2.5 Max: A Deep Dive and Practical Application

Key Concepts:

• Quen 2.5 Max: Alibaba's latest large language model, positioned as a competitor to GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.
• Mixture of Experts (MoE): An architecture where only relevant parts of the model are activated for efficiency.
• Token Training: The amount of data used to train the model, measured in tokens.
• Benchmarks: Standardized tests used to evaluate the performance of AI models.
• API (Application Programming Interface): A set of protocols and tools for building software applications, allowing different systems to communicate.
• Cursor: A code editor that integrates with AI models for code generation and assistance.
• Alibaba Cloud Model Studio: A platform for accessing and using AI models, including Quen 2.5 Max.

1. Introduction to Quen 2.5 Max

• Alibaba has released Quen 2.5 Max, their most advanced model, generating significant interest.
• Quen 2.5 Max is a generalist model, not a reasoning model like DeepSeek R1 or OpenAI's models.
• It is designed to compete with GPT-4o, Claude 3.5 Sonnet, and DeepSeek V3.

2. Key Features and Technical Details

• Mixture of Experts (MoE): Quen 2.5 Max utilizes a Mixture of Experts architecture for efficiency.
• Training Data: Trained on 20 trillion tokens, surpassing GPT-4o's 13 trillion tokens by 35%.
• Fine-tuning: Enhanced model accuracy and alignment with human feedback through fine-tuning.

3. Benchmark Performance

• Quen 2.5 Max appears to outperform other flagship models like DeepSeek V3, Llama 3.1 (400B), GPT-4o, and Claude 3.5 Sonnet in benchmarks.
• The video raises concerns about models being specifically trained to excel in benchmarks, questioning their real-world performance for specific use cases.
• The presenter emphasizes that benchmark results are only valuable if the model performs well in practical applications.

4. Accessing Quen 2.5 Max

• The API and chat interface are currently accessible for free.
• Users need to create an account on the Quen chat platform and ensure "Quen 2.5 Max" is selected.

5. Testing the Chat Interface

• The interface resembles the GPT interface.
• The video tests various features: image generation, artifact creation (code generation), and web search.

5.1 Image Generation

• Prompt: "Generate a knight riding on a pink unicorn with piercing blue eyes. The knight is smiling and waving to the camera."
• The model successfully generated an image with a knight, a pink unicorn, and five fingers on the knight's hand.
• The presenter notes the impressive quality, especially regarding hand generation, a common challenge for AI image models.

5.2 Artifact Creation (Code Generation)

• Prompt: "Create a snake game and let me play it. When I reach the end of the container, I want the snake to appear on the other side of the screen."
• The model generated a playable snake game with the specified feature of the snake reappearing on the opposite side of the screen when reaching the edge.
• The presenter notes the quick token generation and successful one-shot creation of the game.

5.3 Web Search

• Prompt: "Search for articles on the web to learn what happened to OpenAI and the stock market when DeepSeek released its new model DeepSeek V3."
• The model provided links to relevant news articles and summarized the impact of DeepSeek V3's release on OpenAI and the stock market.
• The summary highlighted concerns about competition in the AI sector and the implications of open-source models.

5.4 Project Planning

• Prompt: "I want to create a simple full-stack image conversion SaaS with Next.js and NeonDB. Please play the role of an experienced senior dev and project planner... I need you to ask clarifying questions... and then reverse engineer what libraries, tools, providers, features, database schemas, tables I would need..."
• The model asked clarifying questions about the project's scope, target users, and features.
• It generated a project overview, including target audience, features (image format conversion, compression, free tier, paid subscription, user authentication), technology stack (Next.js, Tailwind, Recoil/TanStack Query, NextAuth, Shrp.js, NeonDB, AWS S3, Stripe), and a basic database schema.
• The presenter notes that the generated requirements document is a good starting point but requires further iteration.

6. Accessing the API via Alibaba Cloud

• API access is available through the Alibaba Cloud Model Studio API.
• Steps:
  1. Sign up for an Alibaba Cloud account.
  2. Activate the Model Studio service.
  3. Generate an API key.
• Users receive a large number of free tokens (around 50 million) during a promotional phase.
• The video highlights the availability of multiple models, including "Quen Max High Performance" and "Quen Max Latest," but the presenter is unsure of the difference between them.
• The presenter selects "Quen Max Latest" for the demonstration.
• The pricing is mentioned as 0.0016 per 1000 tokens.

7. Connecting Quen 2.5 Max to Cursor

• Steps:
  1. Obtain the model name ("quen-max-latest") and API key from the Alibaba Cloud console.
  2. In Cursor, go to Settings > Models.
  3. Add a new model with the name "quen-max-latest" and paste the API key.
  4. Obtain the base URL for the SDK from the Alibaba Cloud console (API example section).
  5. Paste the base URL into Cursor and save the settings.
  6. Verify the connection.

8. Testing Quen 2.5 Max in Cursor

• The presenter tests Quen 2.5 Max in Cursor with various prompts.

8.1 Memory Game in Python

• Prompt: "Could you please go ahead and create a memory game in Python with a graphical user interface... classic memory game where I have to click on different cards and I have to find the same images..."
• The model generated Python code for a memory game using the Tkinter library.
• The presenter encountered an error related to missing image files and modified the prompt to use numbers instead of images.
• The game was functional, allowing the user to flip cards and match pairs.

8.2 Flask Application with Confetti

• Prompt: "Please create a Flask application that is just a simple button on the screen and when the user clicks it confetti appears..."
• The model generated code for a Flask application with a button that triggers confetti using the "canvas-confetti" JavaScript library.
• The presenter ran the application and demonstrated the confetti effect upon clicking the button.

8.3 Content Generator (Lovable Example)

• The presenter attempts to recreate a content generator application found on Lovable.
• The model generates code, but the presenter notes that the code quality is not ideal, with everything placed in a single file.
• Despite the shortcomings, the presenter acknowledges that the generated code could serve as a decent starting point for further development.

9. Conclusion

• The video provides an overview of Quen 2.5 Max, its features, and its performance in various tasks.
• The presenter demonstrates how to access the API and integrate it with Cursor for code generation and assistance.
• While the model shows promise, the presenter emphasizes the need for iterative refinement and human oversight in practical applications.