GLM-4

Overview

GLM-4 is the fourth generation of the General Language Model (GLM) series developed by Zhipu AI (Beijing Zhipu Huazhang Technology Co., Ltd.). It is a large language model with 130 billion parameters, trained on a diverse corpus of Chinese and English text. The model is designed to handle a wide range of natural language processing tasks, including text generation, reasoning, translation, and code synthesis. GLM-4 supports a context window of up to 128K tokens, enabling it to process long documents and maintain coherent conversations over extended interactions.

Zhipu AI offers GLM-4 through both a commercial API (via the Zhipu AI Open Platform) and open-source releases on platforms like Hugging Face and GitHub. The open-source version, GLM-4-9B-Chat, is a smaller 9-billion-parameter variant intended for community experimentation and fine-tuning. The full 130B model is available via API with different pricing tiers.

Capabilities

GLM-4 demonstrates strong performance across several key areas:

• Long-Context Understanding: With a 128K token context window, GLM-4 can process entire books, lengthy reports, or extended dialogues without losing coherence. This is achieved through advanced attention mechanisms and efficient memory management.
• Multilingual Support: The model is bilingual, with native proficiency in both Chinese and English. It can translate between the two languages and handle code-switching naturally.
• Reasoning and Problem-Solving: GLM-4 shows strong capabilities in logical reasoning, mathematical problem-solving, and common-sense reasoning. It performs well on benchmarks like GSM8K, MATH, and MMLU.
• Code Generation: The model can generate code in multiple programming languages (Python, Java, C++, etc.) and assist with debugging, explanation, and refactoring.
• Multimodal Extensions: While GLM-4 itself is text-only, Zhipu AI has released multimodal variants (e.g., GLM-4V) that integrate vision understanding, allowing the model to process images and answer questions about visual content.
• Agent and Tool Use: GLM-4 supports function calling and tool integration, enabling it to interact with external APIs, databases, and software tools. This makes it suitable for building autonomous agents and workflow automation.

Use cases

GLM-4 can be applied in various domains:

• Customer Service: Deploy as a bilingual chatbot for customer support, handling queries in Chinese and English with long conversation history.
• Content Creation: Generate articles, reports, marketing copy, and social media posts in both languages.
• Education: Assist with tutoring, explain complex concepts, and generate practice problems.
• Software Development: Aid in code generation, debugging, and documentation.
• Research: Summarize academic papers, extract information from long documents, and assist with literature reviews.
• Enterprise Automation: Build agents that interact with internal tools, databases, and APIs to automate workflows.

License & deployment

• License: The open-source GLM-4-9B-Chat model is released under the Apache 2.0 license, allowing for commercial use, modification, and redistribution. The full 130B model is available via API under Zhipu AI's terms of service, which include usage limits and pricing.
• Deployment: The 9B model can be deployed on consumer-grade GPUs (e.g., NVIDIA RTX 3090/4090) using frameworks like Transformers, vLLM, or llama.cpp. The 130B model requires high-end server hardware (multiple A100 or H100 GPUs) and is typically accessed via API. Zhipu AI provides a cloud API with pay-as-you-go pricing.
• Hardware Considerations: For local deployment of the 9B model, at least 16GB of VRAM is recommended for full precision, or 8GB with quantization (e.g., 4-bit). The 130B model requires approximately 260GB of VRAM in FP16, necessitating multi-GPU setups.

Alternatives

• GPT-4: OpenAI's flagship model, offering similar capabilities but with broader language support and a larger ecosystem. GPT-4 is closed-source and API-only.
• Llama 3: Meta's open-source model family, available in 8B and 70B sizes. Llama 3 is English-only but has strong performance and a permissive license.
• Qwen: Alibaba's Qwen series (e.g., Qwen2.5-72B) is a strong competitor for Chinese-English tasks, with open-source models and API access.
• DeepSeek: DeepSeek-V2 is a Mixture-of-Experts model with 236B total parameters, offering competitive performance and open-source availability.
• Mistral: Mistral AI's models (e.g., Mistral Large) are known for efficiency and strong reasoning, with both open and commercial options.

FAQ

Q: Is GLM-4 fully open-source? A: Only the smaller 9B variant (GLM-4-9B-Chat) is open-source under Apache 2.0. The full 130B model is available via API.

Q: What is the context length of GLM-4? A: The model supports up to 128K tokens in its context window.

Q: Can GLM-4 handle images? A: The base GLM-4 is text-only, but Zhipu AI offers a multimodal variant called GLM-4V that can process images.

Q: What hardware is needed to run GLM-4 locally? A: For the 9B model, a GPU with at least 16GB VRAM (e.g., RTX 3090) is recommended. The 130B model requires multiple high-end GPUs.

Q: Does GLM-4 support function calling? A: Yes, GLM-4 supports function calling and tool use, enabling integration with external APIs.

Q: How does GLM-4 compare to GPT-4? A: GLM-4 is competitive in Chinese and English tasks, especially for long-context scenarios. However, GPT-4 has a broader ecosystem and more extensive benchmark performance.

Q: Is there a free tier for the API? A: Zhipu AI offers a free trial with limited credits. After that, usage is billed per token.

Q: Can I fine-tune GLM-4? A: Yes, the open-source 9B model can be fine-tuned using standard techniques like LoRA or full fine-tuning.