SmolVLM-500M-Instruct
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Smolvlm 500M Instruct
Overview :
SmolVLM-500M, developed by Hugging Face, is a lightweight multimodal model that belongs to the SmolVLM series. Based on the Idefics3 architecture, it focuses on efficient image and text processing tasks. The model can accept image and text inputs in any order and generate text outputs, making it suitable for tasks such as image description and visual question answering. Its lightweight design allows it to operate on resource-constrained devices while maintaining strong performance in multimodal tasks. The model is licensed under the Apache 2.0 license, enabling open-source and flexible usage scenarios.
Target Users :
This model is designed for developers and researchers who need to run multimodal tasks on resource-constrained devices, especially in scenarios requiring quick processing of image and text inputs to generate text outputs, such as mobile applications, embedded devices, or applications with high real-time demands.
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Use Cases
Quickly generate image descriptions on mobile devices to help users understand image content.
Provide visual question answering capabilities for image recognition applications to enhance user experience.
Implement simple text transcription functions on embedded devices for recognizing text in images.
Features
Supports image description: Capable of generating accurate descriptions of image content.
Visual question answering: Can answer questions related to images.
Text transcription: Able to transcribe text content found within images.
Lightweight architecture: Suitable for running on-device with minimal resource consumption.
Efficient image encoding: Enhances efficiency through large image blocks and visual token encoding.
Supports various multimodal tasks: Such as story creation based on visual content.
Open-source license: Based on Apache 2.0 license, facilitating freedom for developers to use and improve.
Low memory requirements: Requires only 1.23GB of GPU memory to run inference on a single image.
How to Use
1. Load the model and processor using the transformers library: Use AutoProcessor and AutoModelForVision2Seq to load the pretrained model.
2. Prepare input data: Combine images and text queries into a single input message.
3. Process the input: Use the processor to convert the input data into a format acceptable by the model.
4. Run inference: Pass the processed input to the model to generate text output.
5. Decode the output: Convert the generated text IDs back into readable text content.
6. Fine-tune the model if necessary: Use the provided fine-tuning tutorial to optimize the model's performance for specific tasks.
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