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SmolVLM2-256M-Video

SmolVLM2-256M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.38GB of GPU RAM for video inference. This efficiency makes it particularly well-suited for on-device applications that require specific domain fine-tuning and computational resources may be limited.

Model Summary

Developed by: Hugging Face 🤗
Model type: Multi-modal model (image/multi-image/video/text)
Language(s) (NLP): English
License: Apache 2.0
Architecture: Based on Idefics3 (see technical summary)

Resources

Demo: Video Highlight Generator
Blog: Blog post

Uses

SmolVLM2 can be used for inference on multimodal (video / image / text) tasks where the input consists of text queries along with video or one or more images. Text and media files can be interleaved arbitrarily, enabling tasks like captioning, visual question answering, and storytelling based on visual content. The model does not support image or video generation.

To fine-tune SmolVLM2 on a specific task, you can follow the fine-tuning tutorial.

Evaluation

We evaluated the performance of the SmolVLM2 family on the following scientific benchmarks:

Size	Video-MME	MLVU	MVBench
2.2B	52.1	55.2	46.27
500M	42.2	47.3	39.73
256M	33.7	40.6	32.7

How to get started

You can use transformers to load, infer and fine-tune SmolVLM. Make sure you have num2words, flash-attn and latest transformers installed. You can load the model as follows.

python

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model_path = "HuggingFaceTB/SmolVLM2-256M-Video-Instruct"
processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2"
).to("cuda")

Simple Inference

You preprocess your inputs directly using chat templates and directly passing them

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
            {"type": "text", "text": "Can you describe this image?"},            
        ]
    },
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
)
print(generated_texts[0])

Video Inference

To use SmolVLM2 for video inference, make sure you have decord installed.

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "path": "path_to_video.mp4"},
            {"type": "text", "text": "Describe this video in detail"}
        ]
    },
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
)

print(generated_texts[0])

Multi-image Interleaved Inference

You can interleave multiple media with text using chat templates.

python

import torch


messages = [
    {
        "role": "user",
        "content": [
          {"type": "text", "text": "What is the similarity between these two images?"},
          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"},            
        ]
    },
]
inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device, dtype=torch.bfloat16)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
generated_texts = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
)
print(generated_texts[0])

Model optimizations

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)
- Critical automated decision-making
- Generating unreliable factual content
Malicious Activities:
- Spam generation
- Disinformation campaigns
- Harassment or abuse
- Unauthorized surveillance

License

SmolVLM2 is built upon SigLIP as image encoder and SmolLM2 for text decoder part.

We release the SmolVLM2 checkpoints under the Apache 2.0 license.

Citation information

You can cite us in the following way:

bibtex

@article{marafioti2025smolvlm,
  title={SmolVLM: Redefining small and efficient multimodal models}, 
  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},
  journal={arXiv preprint arXiv:2504.05299},
  year={2025}
}

Training Data

SmolVLM2 used 3.3M samples for training originally from ten different datasets: LlaVa Onevision, M4-Instruct, Mammoth, LlaVa Video 178K, FineVideo, VideoStar, VRipt, Vista-400K, MovieChat and ShareGPT4Video. In the following plots we give a general overview of the samples across modalities and the source of those samples.

Data Split per modality

Data Type	Percentage
Image	34.4%
Text	20.2%
Video	33.0%
Multi-image	12.3%

Granular dataset slices per modality

Text Datasets

Dataset	Percentage
llava-onevision/magpie_pro_ft3_80b_mt	6.8%
llava-onevision/magpie_pro_ft3_80b_tt	6.8%
llava-onevision/magpie_pro_qwen2_72b_tt	5.8%
llava-onevision/mathqa	0.9%

Multi-image Datasets

Dataset	Percentage
m4-instruct-data/m4_instruct_multiimage	10.4%
mammoth/multiimage-cap6	1.9%

Image Datasets

Dataset	Percentage
llava-onevision/other	17.4%
llava-onevision/vision_flan	3.9%
llava-onevision/mavis_math_metagen	2.6%
llava-onevision/mavis_math_rule_geo	2.5%
llava-onevision/sharegpt4o	1.7%
llava-onevision/sharegpt4v_coco	1.5%
llava-onevision/image_textualization	1.3%
llava-onevision/sharegpt4v_llava	0.9%
llava-onevision/mapqa	0.9%
llava-onevision/qa	0.8%
llava-onevision/textocr	0.8%

Video Datasets

Dataset	Percentage
llava-video-178k/1-2m	7.3%
llava-video-178k/2-3m	7.0%
other-video/combined	5.7%
llava-video-178k/hound	4.4%
llava-video-178k/0-30s	2.4%
video-star/starb	2.2%
vista-400k/combined	2.2%
vript/long	1.0%
ShareGPT4Video/all	0.8%