SmolVLM is a compact open multimodal model that accepts arbitrary sequences of image and text inputs to produce text outputs. Designed for efficiency, SmolVLM can answer questions about images, describe visual content, create stories grounded on multiple images, or function as a pure language model without visual inputs. Its lightweight architecture makes it suitable for on-device applications while maintaining strong performance on multimodal tasks.
Model Summary
Developed by: Hugging Face 🤗
Model type: Multi-modal model (image+text)
Language(s) (NLP): English
License: Apache 2.0
Architecture: Based on Idefics3 (see technical summary)
SmolVLM can be used for inference on multimodal (image + text) tasks where the input comprises text queries along with one or more images. Text and images can be interleaved arbitrarily, enabling tasks like image captioning, visual question answering, and storytelling based on visual content. The model does not support image generation.
To fine-tune SmolVLM on a specific task, you can follow the fine-tuning tutorial.
Technical Summary
SmolVLM leverages the lightweight SmolLM2 language model to provide a compact yet powerful multimodal experience. It introduces several changes compared to previous Idefics models:
Image compression: We introduce a more radical image compression compared to Idefics3 to enable the model to infer faster and use less RAM.
Visual Token Encoding: SmolVLM uses 81 visual tokens to encode image patches of size 384×384. Larger images are divided into patches, each encoded separately, enhancing efficiency without compromising performance.
More details about the training and architecture are available in our technical report.
Misuse and Out-of-scope Use
SmolVLM is not intended for high-stakes scenarios or critical decision-making processes that affect an individual's well-being or livelihood. The model may produce content that appears factual but may not be accurate. Misuse includes, but is not limited to:
Prohibited Uses:
Evaluating or scoring individuals (e.g., in employment, education, credit)
We release the SmolVLM checkpoints under the Apache 2.0 license.
Training Details
Training Data
The training data comes from The Cauldron and Docmatix datasets, with emphasis on document understanding (25%) and image captioning (18%), while maintaining balanced coverage across other crucial capabilities like visual reasoning, chart comprehension, and general instruction following.
Example Image
Evaluation
Model
MMMU (val)
MathVista (testmini)
MMStar (val)
DocVQA (test)
TextVQA (val)
Min GPU RAM required (GB)
SmolVLM
38.8
44.6
42.1
81.6
72.7
5.02
Qwen-VL 2B
41.1
47.8
47.5
90.1
79.7
13.70
InternVL2 2B
34.3
46.3
49.8
86.9
73.4
10.52
PaliGemma 3B 448px
34.9
28.7
48.3
32.2
56.0
6.72
moondream2
32.4
24.3
40.3
70.5
65.2
3.87
MiniCPM-V-2
38.2
39.8
39.1
71.9
74.1
7.88
MM1.5 1B
35.8
37.2
0.0
81.0
72.5
NaN
Citation information
You can cite us in the following way:
bibtex
1@article{marafioti2025smolvlm,
2 title={SmolVLM: Redefining small and efficient multimodal models},
3 author={Andrés Marafioti and Orr Zohar and Miquel Farré and Merve Noyan and Elie Bakouch and Pedro Cuenca and Cyril Zakka and Loubna Ben Allal and Anton Lozhkov and Nouamane Tazi and Vaibhav Srivastav and Joshua Lochner and Hugo Larcher and Mathieu Morlon and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
4 journal={arXiv preprint arXiv:2504.05299},
5 year={2025}
6}
