Introduction

The landscape of large language models (LLMs) is shifting from monolithic, closed-source giants toward highly capable, specialized open-source alternatives that developers can deploy on their own infrastructure. Among the most significant contributors to this movement is THUDM, the Knowledge Engineering Group at Tsinghua University. Their latest release, GLM-4, represents a major milestone in the evolution of open-source artificial intelligence. With over 10k GitHub stars and a reputation for matching the performance of GPT-4 in specific bilingual benchmarks, GLM-4 is not just another model; it is a comprehensive ecosystem designed for tool use, long-context reasoning, and multimodal understanding.

What Is GLM-4?

GLM-4 is a generative pre-trained language model family that includes various sizes and specializations, most notably the GLM-4-9B and its multimodal counterpart, GLM-4V-9B. Developed by the THUDM team, the project is the successor to the highly successful ChatGLM and GLM-3 series. It is designed to be a high-performance bilingual model, excelling in both Chinese and English tasks while maintaining efficiency that allows it to run on consumer-grade hardware. The project uses a mixture of licenses: the code itself is under the Apache 2.0 license, while the model weights are governed by a specific Model License that allows for free research and academic use, with commercial applications requiring a separate agreement for larger scales.

Why GLM-4 Matters

The significance of GLM-4 lies in its ability to bridge the gap between open-source accessibility and frontier-level performance. Many open-source models struggle with long-context coherence or complex function calling, but GLM-4 addresses these directly. By supporting a 128k token context window, it allows developers to process entire books, massive codebases, or complex legal documents in a single prompt. Furthermore, its specialized training for “All Tools”—which includes web browsing, Python interpretation, and function calling—makes it an ideal candidate for building autonomous agents that need to interact with the real world rather than just predicting the next word in a sentence.

Key Features

• 128k Context Window: GLM-4 supports inputs up to 128,000 tokens, enabling high-fidelity processing of massive datasets without losing context or requiring complex RAG (Retrieval-Augmented Generation) pipelines for moderately large files.
• Multimodal Capabilities: Through GLM-4V-9B, the model can perceive and understand visual information. It achieves state-of-the-art performance for its parameter size on benchmarks like MMMU, surpassing many larger models in visual reasoning and OCR tasks.
• Native Tool Calling: Unlike models that require prompt engineering to use tools, GLM-4 is fine-tuned to emit structured calls for external functions, enabling it to act as a controller for APIs, databases, and local scripts.
• Superior Bilingual Tokenization: The model utilizes a highly optimized tokenizer that is significantly more efficient for Chinese text than standard Western-centric models, resulting in faster inference speeds and lower memory consumption for Asian languages.
• High-Precision Quantization: GLM-4 supports official 4-bit and 8-bit quantization methods, allowing the 9B model to fit into the VRAM of common consumer GPUs like the NVIDIA RTX 3060 or 4070 without massive drops in performance.
• Advanced Reasoning: The model shows marked improvements in mathematical reasoning and coding capabilities, scoring high on HumanEval and GSM8K benchmarks compared to other models in the sub-10B category.

How GLM-4 Compares

In the competitive field of open-source LLMs, GLM-4 competes directly with Meta’s Llama 3 and Alibaba’s Qwen 2. While Llama 3 has a massive global ecosystem, GLM-4 provides a distinct advantage in bilingual tasks and native long-context support that was lacking in Llama’s initial 8B releases. The following table highlights the core differences between these leading models.

Comparing these models reveals that GLM-4 is particularly well-suited for developers who need high density in information processing. While Llama 3 has broader community support for fine-tuning wrappers, GLM-4’s specialized tokenizer makes it more cost-effective for non-English applications. Its performance on the LongBench benchmark proves it can maintain high accuracy across its entire context window, a feat many smaller models struggle with due to attention decay.

Getting Started: Installation

GLM-4 is primarily designed for Python environments and can be deployed via several methods including the Transformers library, vLLM for high-throughput serving, or Ollama for simplified local use. Below are the steps for a standard installation from source.

Prerequisites

Ensure you have Python 3.10 or higher and a modern version of PyTorch. You will also need approximately 18GB of VRAM for the 9B model in full precision, or 6-10GB for quantized versions.

Installation from PyPI

pip install torch tiktoken transformers accelerate sentencepiece

Cloning the Repository

git clone https://github.com/THUDM/GLM-4.git
cd GLM-4
pip install -r requirements.txt

How to Use GLM-4

The easiest way to interact with GLM-4 is using the Hugging Face Transformers API. THUDM provides pre-trained weights for the base, chat, and visual models. The workflow typically involves loading the tokenizer and the model, then formatting the input as a conversation.

The model follows a specific prompt template to handle its multi-turn conversation and tool-calling capabilities. Using the `apply_chat_template` method provided by Transformers is the recommended approach to ensure the special tokens like `<|user|>` and `<|assistant|>` are placed correctly.

Code Examples

Basic Chat Implementation

This example shows how to load the GLM-4-9B-Chat model and generate a simple response.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True)

query = "Explain the concept of quantum entanglement in simple terms."
inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True).to(device)

model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).to(device).eval()

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512)
    outputs = outputs[:, inputs['input_ids'].shape[1]:]
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Visual Understanding with GLM-4V

The multimodal model allows you to pass an image along with a text query.

# Assuming GLM-4V environment is set up
from PIL import Image
image = Image.open("demo.jpg").convert("RGB")
query = "Describe the contents of this image and identify any text visible."
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True).to(device)
# Model generation follows same pattern as above

Advanced Configuration

For high-performance deployments, GLM-4 supports several advanced configurations. You can use Flash Attention 2 to speed up the processing of long sequences, which significantly reduces the quadratic memory growth typical of standard self-attention. Additionally, if you are memory-constrained, you can load the model in 4-bit mode using the bitsandbytes library by passing load_in_4bit=True to the from_pretrained method. This allows the model to run on GPUs with as little as 6GB of VRAM, making it accessible for edge devices.

Real-World Use Cases

• Bilingual Customer Support: Companies operating in both Chinese and English markets use GLM-4 to power unified support bots that can handle technical queries in both languages with equal nuance.
• Automated Document Summarization: The 128k context window makes GLM-4 ideal for legal and academic researchers who need to summarize entire research papers or contracts without missing critical details found in the middle of the text.
• Visual Inspection and OCR: Using GLM-4V, developers in logistics and manufacturing can automate the identification of labels, damaged goods, or specific parts from camera feeds.
• Autonomous Coding Assistants: Because of its strong performance on HumanEval and native tool-calling, GLM-4 is used in IDE extensions to generate, debug, and explain code within complex project structures.

Contributing to GLM-4

THUDM maintains an active development cycle and welcomes community contributions. To contribute, users should review the CONTRIBUTING.md file in the repository, which outlines the process for reporting bugs and submitting pull requests. The project encourages developers to share their fine-tuned versions of GLM-4 or optimization scripts for different hardware backends. As an academic-led project, there is a strong emphasis on reproducibility and factual correctness in the model’s outputs.

Community and Support

The GLM-4 community is primarily active on GitHub Discussions and dedicated social channels in China. For international developers, the Hugging Face model cards serve as the primary hub for sharing benchmarks and inference scripts. The repository also includes a comprehensive FAQ and a basic_demo folder containing CLI and web-based interaction scripts to help new users troubleshoot common setup issues.

Conclusion

GLM-4 stands as a testament to the power of open-source research, providing a robust, multimodal, and long-context capable alternative to proprietary models. Whether you are building a complex bilingual agent, a document analysis tool, or a visual reasoning system, GLM-4 offers the flexibility and performance required for modern AI applications. While it requires significant computational resources to run at full fidelity, its quantization options and efficient architecture make it one of the most versatile models in the 9B parameter class.

As the AI field continues to evolve, the ability to run such high-quality models locally is invaluable for data privacy and customizability. We recommend starting with the GLM-4-9B-Chat model on Hugging Face to evaluate its performance on your specific tasks before scaling up to more complex multimodal or fine-tuned deployments.

Resources

• Official GLM-4 GitHub Repository
• THUDM Hugging Face Organization
• Official THUDM Blog and Announcements