GLM-4.5

GLM-4.5 can be installed via pip with pip install transformers torch. Download the model from Hugging Face Hub using transformers.AutoModel.from_pretrained("THUDM/glm-4-9b"). For production deployment, use Docker containers or cloud services like AWS/Azure with GPU support for optimal performance.

GLM-4.5 supports multiple API interfaces: REST API for web applications, Python SDK for direct integration, and OpenAI-compatible endpoints for easy migration. The model includes native function calling capabilities, allowing it to interact with external tools and services through structured API calls.

GLM-4.5 ranks #3 globally, closely matching GPT-4's performance while being completely open-source. It outperforms GPT-4 on certain coding benchmarks and offers a longer 128K context window. The key advantage is the MIT license allowing unlimited commercial use without API costs or data privacy concerns.

The Mixture-of-Experts (MoE) architecture allows GLM-4.5 to have 355B total parameters while only activating 32B per inference. This provides the knowledge capacity of a massive model with the efficiency of a smaller one, resulting in 8x better performance per computational cost compared to dense models of similar capability.

GLM-4.5 supports code generation in Python, JavaScript, Java, C++, Go, Rust, and many other languages. It can work with popular frameworks like React, Django, Flask, and provides full-stack development capabilities. The model also integrates with development tools, IDEs, and can execute code through its tool-calling abilities.

Yes, GLM-4.5 is released under the MIT License, which allows unlimited commercial use without licensing fees. You can deploy it in production, modify the model, create derivative works, and sell applications built with GLM-4.5. This makes it one of the most commercially-friendly advanced LLMs available.

GLM-4.5 supports multiple fine-tuning approaches: LoRA (Low-Rank Adaptation) for efficient training, full parameter fine-tuning for maximum customization, and RLHF (Reinforcement Learning from Human Feedback) for alignment. Zhipu AI provides detailed documentation and example scripts for custom training on domain-specific data.

GLM-4.5 requires 80GB+ GPU memory for full model inference (e.g., A100 80GB). For smaller deployments, use GLM-4.5-Air (12B active parameters) which runs on 24GB GPUs. Optimization techniques include quantization (8-bit, 4-bit), model parallelism across multiple GPUs, and CPU offloading for memory-constrained environments.

GLM-4.5 was trained on 24+ languages with exceptional English-Chinese bilingual capabilities. It outperforms GPT-4 on Chinese alignment benchmarks and provides strong performance in other major languages including Spanish, French, German, Japanese, and Korean. The model can handle translation, multilingual dialogue, and code comments in multiple languages.

GLM-4.5 has extensive community support through GitHub (700,000+ developers), Hugging Face Hub with model cards and examples, official documentation at Zhipu AI's website, Discord community for real-time help, and academic papers detailing the architecture. Regular updates and community contributions make it a well-supported open-source project.