GLM-4 9B Sets Benchmark for Open-Source Language Models

Last Updated on July 4, 2024 by SPN Editor

In the ever-evolving landscape of artificial intelligence (AI), language models play a pivotal role. These sophisticated algorithms, trained on vast amounts of text data, enable machines to understand and generate human-like language. Recently, Tsinghua University’s Knowledge Engineering Group (KEG) unveiled a groundbreaking language model: GLM-4 9B. This open-source marvel has set new standards, outperforming industry giants and advancing the field of natural language processing (NLP).

The Birth of GLM-4 9B

At its core, GLM-4 9B is a massive language model, meticulously trained on an unprecedented 10 trillion tokens across 26 languages. Developed by the Tsinghua Deep Model (THUDM) team, this model represents a significant milestone in AI research. Let’s delve into what makes GLM-4 9B exceptional:

1. Versatility and Capabilities: GLM-4 9B isn’t just another language model; it’s a Swiss Army knife for NLP. Its capabilities include:
   • Multi-Round Dialogue: Engaging in extended conversations in both Chinese and English.
   • Code Execution: Understanding and executing code snippets.
   • Web Browsing: Navigating the internet.
   • Custom Tool Invocation: Through Function Call, enabling tailored interactions.
2. Cutting-Edge Architecture: GLM-4 9B’s architecture incorporates state-of-the-art deep learning techniques. Attention mechanisms and transformer architectures form its backbone, ensuring efficient information processing.
3. Context Window: In its base version, it handles a context window of up to 128,000 tokens. But wait, there’s more! A specialized variant extends this to an astonishing 1 million tokens. Imagine the possibilities!
4. Vision Tasks: It stands out by supporting high-resolution vision tasks, accommodating images up to 1198 x 1198 pixels. It’s not just about text; it sees the bigger picture.
5. Multilingual Prowess: Fluent in 26 languages, it bridges linguistic gaps. Whether you’re conversing in Mandarin, Spanish, or Russian, it’s got you covered.

The Triumphs

GLM-4 9B isn’t just hype; it delivers results:

• Superior Performance: Evaluations across diverse datasets reveal GLM-4 9B’s exceptional accuracy. It outperforms existing models, leaving no room for doubt.
• GPT-4 and Gemini, Beware: It surpasses these industry heavyweights. In vision tasks, it even outshines Gemini Pro.
• Open-Source and Commercially Friendly: Developers, researchers, and businesses rejoice! It is open-source, with flexible commercial licensing. Opportunities abound.

Applications Galore

The applications are boundless:

• NLP: Sentiment analysis, chatbots, and language understanding.
• Computer Vision: Image classification, object detection, and more.
• Code Generation: Writing code snippets on demand.

Seamless Integration

GLM-4 9B plays well with others. Its seamless integration with the Transformers library simplifies adoption and deployment. No headaches, just progress.

A New Era Dawns

Tsinghua University’s GLM-4 9B isn’t just a model; it’s a beacon. As we step into the future of AI, it sets the benchmark for open-source language models. 

It is a notable model in the GLM-4 series, an advanced language model framework developed by Zhipu AI. This model stands out due to its impressive performance and extensive features. In various evaluations across domains such as semantics, mathematics, reasoning, code comprehension, and general knowledge, the GLM-4-9B, along with its human-preference-aligned variant, GLM-4 9B-Chat, has demonstrated superior performance compared to the previous Llama-3-8B model.

This GLM Chat is not just limited to multi-turn conversations; it also includes capabilities like web browsing, code execution, custom tool invocation through Function Call, and long-text reasoning, supporting contexts up to 128K. This model is designed to be multilingual, covering 26 languages including Japanese, Korean, and German.

Additionally, the GLM series includes other variants such as the GLM-4 9B-Chat-1M, which supports context lengths up to 1 million tokens (around 2 million Chinese characters), and the GLM-4V-9B, a multimodal model excelling in high-resolution bilingual conversations and various multimodal tasks.

Evaluation results have shown that GLM-4 9B-Chat surpasses Llama-3-8B-Instruct in alignment benchmarks, machine translation, inference evaluation, and more. Similarly, the base GLM-4 9B model outperforms Llama-3-8B in benchmarks related to code evaluation, general-purpose question answering, and others. Experiments with 1 million context lengths further demonstrate the robustness of GLM-4 9B-Chat in handling extensive texts, highlighting its superior capabilities in long-text experiments.