Maintainer: THUDM

Total Score


Last updated 6/11/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

glm-4v-9b is a large language model developed by THUDM, a leading AI research group. It is part of the GLM (General Language Model) family, which aims to create open, bilingual language models capable of strong performance across a wide range of tasks.

The glm-4v-9b model builds upon the successes of earlier GLM models, incorporating advanced techniques like autoregressive blank infilling and hybrid pretraining objectives. This allows it to achieve impressive results on benchmarks like MMBench-EN-Test, MMBench-CN-Test, and SEEDBench_IMG, outperforming models like GPT-4-turbo-2024-04-09, Gemini 1.0, and Qwen-VL-Max.

Compared to similar large language models, glm-4v-9b stands out for its strong multilingual and multimodal capabilities. It can seamlessly handle both English and Chinese, and has been trained to integrate visual information with text, making it well-suited for tasks like image captioning and visual question answering.

Model Inputs and Outputs


  • Text: The model can accept text input in the form of a conversation, with the user's message formatted as {"role": "user", "content": "query"}.
  • Images: Along with text, the model can also take image inputs, which are passed through the tokenizer using the image field in the input template.


  • Text Response: The model will generate a text response to the provided input, which can be retrieved by decoding the model's output tokens.
  • Conversation History: The model maintains a conversation history, which can be passed back into the model to continue the dialogue in a coherent manner.


The glm-4v-9b model has demonstrated strong performance on a wide range of benchmarks, particularly those testing multilingual and multimodal capabilities. For example, it achieves high scores on the MMBench-EN-Test (81.1), MMBench-CN-Test (79.4), and SEEDBench_IMG (76.8) tasks, showcasing its ability to understand and generate text in both English and Chinese, as well as integrate visual information.

Additionally, the model has shown promising results on tasks like MMLU (58.7), AI2D (81.1), and OCRBench (786), indicating its potential for applications in areas like question answering, image understanding, and optical character recognition.

What Can I Use It For?

The glm-4v-9b model's strong multilingual and multimodal capabilities make it a versatile tool for a variety of applications. Some potential use cases include:

  • Intelligent Assistants: The model's ability to engage in natural language conversations, while also understanding and generating content related to images, makes it well-suited for building advanced virtual assistants that can handle a wide range of user requests.

  • Multimodal Content Generation: Leveraging the model's text-image integration capabilities, developers can create applications that generate multimedia content, such as image captions, visual narratives, or even animated stories.

  • Multilingual Language Understanding: Organizations operating in diverse language environments can use glm-4v-9b to build applications that can seamlessly handle both English and Chinese, enabling improved cross-cultural communication and collaboration.

  • Research and Development: As an open-source model, glm-4v-9b can be a valuable resource for AI researchers and developers looking to explore the latest advancements in large language models and multimodal learning.

Things to Try

One key feature of the glm-4v-9b model is its ability to effectively utilize both textual and visual information. Developers and researchers can experiment with incorporating image data into their applications, exploring how the model's multimodal capabilities can enhance tasks like image captioning, visual question answering, or even image-guided text generation.

Another avenue to explore is the model's strong multilingual performance. Users can try interacting with the model in both English and Chinese, and observe how it maintains coherence and contextual understanding across languages. This can lead to insights on building truly global AI systems that can bridge language barriers.

Finally, the model's impressive benchmark scores suggest that it could be a valuable starting point for fine-tuning or further pretraining on domain-specific datasets. Developers can experiment with adapting the model to their particular use cases, unlocking new capabilities and expanding the model's utility.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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The glm-4-9b is a large language model developed by THUDM, a research group at Tsinghua University. It is part of the GLM (General Language Model) family of models, which are trained using autoregressive blank infilling techniques. The glm-4-9b model has 4.9 billion parameters and is capable of generating human-like text across a variety of domains. Compared to similar models like Llama-3-8B, ChatGLM3-6B-Base, and GLM-4-9B-Chat, the glm-4-9b model demonstrates stronger performance on a range of benchmarks, including MMLU (+8.1%), C-Eval (+25.8%), GSM8K (+8.2%), and HumanEval (+7.9%). Model Inputs and Outputs The glm-4-9b model is a text-to-text transformer, which means it can be used for a variety of natural language processing tasks, including text generation, text summarization, and question answering. Inputs Natural language text prompts Outputs Generated text based on the input prompt Capabilities The glm-4-9b model has shown strong performance on a variety of natural language tasks, including open-ended question answering, common sense reasoning, and mathematical problem-solving. For example, the model can be used to generate coherent and contextually relevant responses to open-ended questions, or to solve complex math problems by breaking them down and providing step-by-step explanations. What Can I Use It For? The glm-4-9b model can be used for a wide range of applications, including: Content Generation**: The model can be used to generate high-quality, human-like text for tasks such as article writing, story generation, and dialogue systems. Question Answering**: The model can be used to answer open-ended questions on a variety of topics, making it useful for building intelligent assistants or knowledge-based applications. Language Understanding**: The model's strong performance on benchmarks like MMLU and C-Eval suggests it can be used for tasks like text summarization, sentiment analysis, and natural language inference. Things to Try One interesting aspect of the glm-4-9b model is its ability to perform well on mathematical problem-solving tasks. Users could try prompting the model with complex math problems and see how it responds, or experiment with combining the model's language understanding capabilities with its ability to reason about numerical concepts. Another avenue to explore is the model's potential for multilingual applications. Since the GLM models are trained on a bilingual (Chinese and English) corpus, the glm-4-9b could be used for tasks that require understanding and generating text in both languages, such as machine translation or cross-lingual information retrieval.

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