README.md

---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
pipeline_tag: image-text-to-text
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-135M-Instruct
- google/siglip-base-patch16-512
---

<img src="https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/SmolVLM_256_banner.png" width="800" height="auto" alt="Image description">

# SmolVLM-256M

SmolVLM-256M is the smallest multimodal model in the world. It accepts arbitrary sequences of image and text inputs to produce text outputs. It's designed for efficiency. SmolVLM can answer questions about images, describe visual content, or transcribe text. Its lightweight architecture makes it suitable for on-device applications while maintaining strong performance on multimodal tasks. It can run inference on one image with under 1GB of GPU RAM.

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

## Resources

- **Demo:** [SmolVLM-256 Demo](https://huggingface.co./spaces/HuggingFaceTB/SmolVLM-256M-Demo)
- **Blog:** [Blog post](https://huggingface.co./blog/smolvlm)

## Uses

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

### Technical Summary

SmolVLM leverages the lightweight SmolLM2 language model to provide a compact yet powerful multimodal experience. It introduces several changes compared to the larger SmolVLM 2.2B model:

- **Image compression:** We introduce a more radical image compression compared to Idefics3 and SmolVLM-2.2B to enable the model to infer faster and use less RAM.
- **Visual Token Encoding:** SmolVLM-256 uses 64 visual tokens to encode image patches of size 512×512. Larger images are divided into patches, each encoded separately, enhancing efficiency without compromising performance.
- **New special tokens:** We added new special tokens to divide the subimages. This allows for more efficient tokenization of the images.
- **Smoller vision encoder:** We went from a 400M parameter siglip vision encoder to a much smaller 93M encoder.
- **Larger image patches:** We are now passing patches of 512x512 to the vision encoder, instead of 384x384 like the larger SmolVLM. This allows the information to be encoded more efficiently.

More details about the training and architecture are available in our technical report.

### How to get started

You can use transformers to load, infer and fine-tune SmolVLM.

```python
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
from transformers.image_utils import load_image

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load images
image = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")

# Initialize processor and model
processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-256M-Instruct")
model = AutoModelForVision2Seq.from_pretrained(
    "HuggingFaceTB/SmolVLM-256M-Instruct",
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager",
).to(DEVICE)

# Create input messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Can you describe this image?"}
        ]
    },
]

# Prepare inputs
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image], return_tensors="pt")
inputs = inputs.to(DEVICE)

# Generate outputs
generated_ids = model.generate(**inputs, max_new_tokens=500)
generated_texts = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
)

print(generated_texts[0])
"""
Assistant: The image depicts a large, historic statue of liberty, located in New York City. The statue is a green, cylindrical structure with a human figure at the top, holding a torch. The statue is situated on a pedestal that resembles the statue of liberty, which is located on a small island in the middle of a body of water. The water surrounding the island is calm, reflecting the blue sky and the statue.
In the background, there are several tall buildings, including the Empire State Building, which is visible in the distance. These buildings are made of glass and steel, and they are positioned in a grid-like pattern, giving them a modern look. The sky is clear, with a few clouds visible, indicating fair weather.
The statue is surrounded by trees, which are green and appear to be healthy. There are also some small structures, possibly houses or buildings, visible in the distance. The overall scene suggests a peaceful and serene environment, typical of a cityscape.
The image is taken during the daytime, likely during the day of the statue's installation. The lighting is bright, casting a strong shadow on the statue and the water, which enhances the visibility of the statue and the surrounding environment.
To summarize, the image captures a significant historical statue of liberty, situated on a small island in the middle of a body of water, surrounded by trees and buildings. The sky is clear, with a few clouds visible, indicating fair weather. The statue is green and cylindrical, with a human figure holding a torch, and is surrounded by trees, indicating a peaceful and well-maintained environment. The overall scene is one of tranquility and historical significance.
"""
```


### Model optimizations

**Precision**: For better performance, load and run the model in half-precision (`torch.bfloat16`) if your hardware supports it.

```python
from transformers import AutoModelForVision2Seq
import torch

model = AutoModelForVision2Seq.from_pretrained(
    "HuggingFaceTB/SmolVLM-Instruct",
    torch_dtype=torch.bfloat16
).to("cuda")
```

You can also load SmolVLM with 4/8-bit quantization using bitsandbytes, torchao or Quanto. Refer to [this page](https://huggingface.co./docs/transformers/en/main_classes/quantization) for other options.

```python
from transformers import AutoModelForVision2Seq, BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForVision2Seq.from_pretrained(
    "HuggingFaceTB/SmolVLM-Instruct",
    quantization_config=quantization_config,
)
```

**Vision Encoder Efficiency**: Adjust the image resolution by setting `size={"longest_edge": N*512}` when initializing the processor, where N is your desired value. The default `N=4` works well, which results in input images of
size 2048×2048. Decreasing N can save GPU memory and is appropriate for lower-resolution images. This is also useful if you want to fine-tune on videos.


## 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

SmolVLM is built upon [SigLIP](https://huggingface.co./google/siglip-base-patch16-512) as image encoder and [SmolLM2](https://huggingface.co./HuggingFaceTB/SmolLM2-135M-Instruct) for text decoder part.

We release the SmolVLM checkpoints under the Apache 2.0 license.

## Training Details

### Training Data

The training data comes from [The Cauldron](https://huggingface.co./datasets/HuggingFaceM4/the_cauldron) and [Docmatix](https://huggingface.co./datasets/HuggingFaceM4/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.
<img src="https://huggingface.co./HuggingFaceTB/SmolVLM-Instruct/resolve/main/mixture_the_cauldron.png" alt="Example Image" style="width:90%;" />



## Evaluation

| Size  | Mathvista | MMMU | OCRBench | MMStar | AI2D  | ChartQA_Test | Science_QA | TextVQA Val | DocVQA Val |
|-------|-----------|------|----------|--------|-------|--------------|------------|-------------|------------|
| 256M  | 35.9      | 28.3 | 52.6     | 34.6   | 47    | 55.8         | 73.6       | 49.9        | 58.3       |
| 500M  | 40.1      | 33.7 | 61       | 38.3   | 59.5  | 63.2         | 79.7       | 60.5        | 70.5       |
| 2.2B  | 43.9      | 38.3 | 65.5     | 41.8   | 64    | 71.6         | 84.5       | 72.1        | 79.7       |