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Smolvlm
SmolVLM
S
Smolvlm
AI ModelDevelopment & Tools#Visual Language Model#Multi-Modal AI#Open Source#Edge Computing#TransformersStandard PicksOpen Source
Overview :
SmolVLM is a compact yet powerful visual language model (VLM) with 2 billion parameters, leading in efficiency and memory usage among similar models. It is fully open-source, with all model checkpoints, VLM datasets, training recipes, and tools released under the Apache 2.0 license. The model is designed for local deployment in browsers or edge devices, reducing inference costs and allowing for user customization.
Target Users :
The target audience includes developers and enterprises that need to deploy visual language models on local or edge devices, particularly those sensitive to model size and inference costs. SmolVLM's compact, efficient, and open-source nature makes it well-suited for resource-constrained environments, such as mobile devices or small servers.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 53.3K
Use Cases
Provide travel recommendations for the Grand Palace in Bangkok using SmolVLM.
Identify areas affected by severe drought based on charts.
Extract due dates and invoice dates from invoices.
Features
Supports multi-modal AI for use in smaller local settings.
Completely open-source, allowing for commercial use and custom deployment.
Low memory footprint, suitable for operation on resource-constrained devices.
High performance, with multiple benchmark results including image encoding efficiency.
Supports video analysis tasks, especially in environments with limited computational resources.
Integrates with VLMEvalKit for evaluation across more benchmarks.
Easily load and use via the Transformers library.
How to Use
1. Visit SmolVLM's Hugging Face page to download the desired model and processor.
2. Load the model and processor using Python and the Transformers library.
3. Prepare input data, including images and text prompts.
4. Format the input data into a model-compatible format using the processor.
5. Generate output using the model, such as describing image content or answering questions related to the image.
6. Decode and post-process the generated output to obtain the final result.
7. (Optional) Fine-tune SmolVLM for specific tasks to enhance performance.
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