glm-4v-9b
Maintainer: THUDM
154
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No 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
Inputs
- 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
imagefield in the input template.
Outputs
- 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.
Capabilities
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:
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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.
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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.
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Multilingual Language Understanding: Organizations operating in diverse language environments can use
glm-4v-9bto 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-9bcan 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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