GLM-4 32B (+ Free APIs) + RooCode & Cline: I'm BLOWN AWAY by this INSANE Model (Beat 32B Coder!)

GLM-4 32B Model: A Deep Dive

Key Concepts:

• GLM-4 32B: A 32 billion parameter coding-focused language model.
• Thudm & Zai: The organizations behind the GLM models (Tsinghua University and Zai company).
• Code Geex: A previous model by Zai, considered less successful.
• GLM-4 Series: Includes GLM-4 32B, GLM-4 9B, GLMZ 132B, and GLMZ 19B models.
• Z1 Models: Reasoning models (GLMZ), less effective than Quen 3.
• Coding Benchmarks: The five coding questions used to evaluate the model's performance.
• Rukode & Klein: IDEs used to test the model's coding capabilities.
• Shad CN: A UI component library for React.
• System Prompts: Instructions given to the model to guide its behavior.
• Novita & OpenRouter: Platforms offering APIs for the GLM-4 32B model.

Model Overview and Performance

The video focuses on the GLM-4 32B model, a coding-focused language model developed by Thudm and Zai. The model is part of the GLM-4 series, which also includes the GLM-4 9B and the reasoning-focused GLMZ models. The GLM-4 32B model stands out due to its impressive coding capabilities, having passed all five coding benchmark questions. The model excels at generating code for complex tasks like creating a butterfly animation, a synth keyboard, a hexagon pattern, a bouncing ball animation, and the Game of Life. While not as strong as models like Gemini 2.5 Pro or Flash, it performs exceptionally well for a local model of its size (32 billion parameters).

Hardware and API Availability

The GLM-4 32B model can be run locally on a MacBook with 32GB of RAM or an RTX 4090. The model weights are available on Hugging Face, and a version with better tool calling is available on Llama. For those who prefer using APIs, Novita offers an API at 24 cents per million tokens (input and output), and a free API is available on OpenRouter with certain limitations.

Practical Application with Rukode

The video demonstrates how to use the GLM-4 32B model in Rukode, an IDE that works well with the model. The process involves creating a profile in Rukode, selecting either the Llama option for local use or the OpenRouter option for API access, and configuring the API accordingly.

Step-by-step process in Rukode:

1. Go to settings and create a new profile.
2. Select "Llama" for local use or "OpenRouter" for API use.
3. Configure the API with the appropriate credentials (free or paid).

Coding Example: Image Cropper Tool

The video showcases the model's capabilities by asking it to build a simple image cropper tool in Next.js. The model attempts to use Shad CN, a UI component library, even though it's not preconfigured in the project. This indicates that the model's training data might have some inconsistencies. However, after manually installing Shad CN, the image cropper tool works reasonably well, demonstrating the model's potential.

Strengths and Weaknesses

Strengths:

• Excellent coding capabilities for a 32B model.
• Can be run locally on consumer-grade hardware.
• Available through various APIs (Novita, OpenRouter).
• Good for simple HTML apps and Python.
• Effective for debugging and simple chat tasks.

Weaknesses:

• Not as strong in React development.
• Prone to making errors.
• May hallucinate or get stuck.
• Sometimes talks in Chinese.

Notable Observations

The model sometimes starts sentences with "I need to do this and that," indicating a unique way of processing tasks. It is also prone to speaking in Chinese, which can be mitigated by using system prompts to guide its behavior.

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Conclusion

The GLM-4 32B model is a promising coding-focused language model that offers impressive performance for its size. It can be run locally or accessed through APIs, making it accessible to a wide range of users. While it has some limitations, its strengths make it a valuable tool for coding tasks, debugging, and simple chat applications. The model is a good base for fine-tuning on specific tasks.