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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- HuggingFaceM4/the_cauldron |
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- HuggingFaceM4/Docmatix |
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pipeline_tag: image-text-to-text |
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language: |
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- en |
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--- |
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# SmolVLM Instruct |
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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. |
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## Model Summary |
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- **Developed by:** Hugging Face 🤗 |
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- **Model type:** Multi-modal model (image+text) |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Architecture:** Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see more details below) |
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## Resources |
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- **Demo:** [SmolVLM Demo](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM) |
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- **Blog:** [More Information Needed] |
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- **Technical Report:** [More Information Needed] |
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- **Repository:** [More Information Needed] |
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## Uses |
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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. |
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To fine-tune SmolVLM on a specific task, you can follow the fine-tuning tutorial. |
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<!-- todo: add link to fine-tuning tutorial --> |
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### Technical Summary |
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SmolVLM leverages the lightweight SmolLM2 language model to provide a compact yet powerful multimodal experience. It introduces several changes compared to previous Idefics models: |
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- **Image compression:** We introduce a more radical image compression compared to Idefics3 to enable the model to infer faster and use less RAM. |
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- **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. |
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More details about the training and architecture are available in our technical report. |
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### How to get started |
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You can use transformers to load, infer and fine-tune SmolVLM. |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoProcessor, AutoModelForVision2Seq |
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from transformers.image_utils import load_image |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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# Load images |
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image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg") |
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image2 = load_image("https://huggingface.co/spaces/merve/chameleon-7b/resolve/main/bee.jpg") |
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# Initialize processor and model |
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processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct") |
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model = AutoModelForVision2Seq.from_pretrained( |
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"HuggingFaceTB/SmolVLM-Instruct", |
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torch_dtype=torch.bfloat16, |
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).to(DEVICE) |
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# Create input messages |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image"}, |
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{"type": "text", "text": "What do we see in this image?"} |
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] |
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}, |
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{ |
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"role": "assistant", |
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"content": [ |
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{"type": "text", "text": "This image shows a city skyline with prominent landmarks."} |
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] |
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}, |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image"}, |
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{"type": "text", "text": "And how about this image?"} |
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] |
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} |
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] |
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# Prepare inputs |
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prompt = processor.apply_chat_template(messages, add_generation_prompt=True) |
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inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt") |
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inputs = inputs.to(DEVICE) |
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# Generate outputs |
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generated_ids = model.generate(**inputs, max_new_tokens=500) |
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generated_texts = processor.batch_decode( |
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generated_ids, |
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skip_special_tokens=True, |
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) |
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print(generated_texts[0]) |
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``` |
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### Model optimizations |
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**Precision**: For better performance, load and run the model in half-precision (`torch.float16` or `torch.bfloat16`) if your hardware supports it. |
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```python |
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from transformers import AutoModelForVision2Seq |
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import torch |
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model = AutoModelForVision2Seq.from_pretrained( |
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"HuggingFaceTB/SmolVLM-Instruct", |
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torch_dtype=torch.bfloat16 |
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).to("cuda") |
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``` |
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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. |
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```python |
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from transformers import AutoModelForVision2Seq, BitsAndBytesConfig |
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import torch |
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quantization_config = BitsAndBytesConfig(load_in_8bit=True) |
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model = AutoModelForVision2Seq.from_pretrained( |
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"HuggingFaceTB/SmolVLM-Instruct", |
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quantization_config=quantization_config, |
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) |
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``` |
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**Vision Encoder Efficiency**: Adjust the image resolution by setting `size={"longest_edge": N*384}` when initializing the processor, where N is your desired value. The default `N=4` works well, which results in input images of |
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size 1536×1536. For documents, `N=5` might be beneficial. 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. |
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## Misuse and Out-of-scope Use |
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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: |
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- Prohibited Uses: |
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- Evaluating or scoring individuals (e.g., in employment, education, credit) |
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- Critical automated decision-making |
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- Generating unreliable factual content |
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- Malicious Activities: |
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- Spam generation |
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- Disinformation campaigns |
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- Harassment or abuse |
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- Unauthorized surveillance |
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### License |
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SmolVLM is built upon [the shape-optimized SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) for text decoder part. |
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We release the SmolVLM checkpoints under the Apache 2.0 license. |
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## Training Details |
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### Training Data |
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The training data is:  |
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#### Speeds, Sizes, Times [optional] |
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TODO |
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## Evaluation |
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TODO |
