transformers onnx safetensors smolvlm image-text-to-text conversational en dataset:HuggingFaceM4/the_cauldron dataset:HuggingFaceM4/Docmatix dataset:lmms-lab/LLaVA-OneVision-Data dataset:lmms-lab/M4-Instruct-Data dataset:HuggingFaceFV/finevideo dataset:MAmmoTH-VL/MAmmoTH-VL-Instruct-12M dataset:lmms-lab/LLaVA-Video-178K dataset:orrzohar/Video-STaR dataset:Mutonix/Vript dataset:TIGER-Lab/VISTA-400K dataset:Enxin/MovieChat-1K_train dataset:ShareGPT4Video/ShareGPT4Video base_model:HuggingFaceTB/SmolVLM-256M-Instruct base_model:quantized:HuggingFaceTB/SmolVLM-256M-Instruct license:apache-2.0 endpoints_compatible region:us
---
base_model:
- HuggingFaceTB/SmolVLM-256M-Instruct
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
---

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM2_banner.png" width="800" height="auto" alt="Image description">

# 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](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary)

## Resources

- **Demo:** [Video Highlight Generator](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator)
- **Blog:** [Blog post](https://huggingface.co/blog/smolvlm2)

## 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](https://github.com/huggingface/smollm/blob/main/vision/finetuning/Smol_VLM_FT.ipynb).

## 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](https://huggingface.co/google/siglip-base-patch16-512) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) 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
@unpublished{marafioti2025smolvlm,
  title = {SmolVLM: Redefining small and efficient multimodal models},
  author = {Marafioti, Andr\'{e}s and Zohar, Orr and Farr\'{e}, Miquel and Noyan, Merve and Bakouch, Elie and Cuenca, Pedro and Zakka, Cyril and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Srivastav, Vaibhav and Lochner, Joshua and Larcher, Hugo and Morlon, Mathieu and Tunstall, Lewis and von Werra, Leandro and Wolf, Thomas},
  year = {2025},
}
```

## Training Data
SmolVLM2 used 3.3M samples for training originally from ten different datasets: [LlaVa Onevision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), [M4-Instruct](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data), [Mammoth](https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M), [LlaVa Video 178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K), [FineVideo](https://huggingface.co/datasets/HuggingFaceFV/finevideo), [VideoStar](https://huggingface.co/datasets/orrzohar/Video-STaR), [VRipt](https://huggingface.co/datasets/Mutonix/Vript), [Vista-400K](https://huggingface.co/datasets/TIGER-Lab/VISTA-400K), [MovieChat](https://huggingface.co/datasets/Enxin/MovieChat-1K_train) and [ShareGPT4Video](https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video).
In the following plots we give a general overview of the samples across modalities and the source of those samples.
<!--
<center><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_data_split.png" width="auto" height="auto" alt="Image description">
</center>

### Details
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_datadetails.png" width="auto" height="auto" alt="Image description"> -->

## 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%       |
