transformers/docs/source/en/model_doc/smolvlm.md

This model was published in HF papers on 2025-04-07 and contributed to Hugging Face Transformers on 2025-02-20.

SmolVLM

Overview

SmolVLM2 (blog post) is an adaptation of the Idefics3 model with two main differences:

• It uses SmolLM2 for the text model.
• It supports multi-image and video inputs

Usage tips

Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.

Videos should not be upsampled.

If do_resize is set to True, the model resizes images so that the longest edge is 4*512 pixels by default. The default resizing behavior can be customized by passing a dictionary to the size parameter. For example, {"longest_edge": 4 * 512} is the default, but you can change it to a different value if needed.

Here's how to control resizing and set a custom size:

image_processor = SmolVLMImageProcessor(do_resize=True, size={"longest_edge": 2 * 512}, max_image_size=512)

Additionally, the max_image_size parameter, which controls the size of each square patch the image is decomposed into, is set to 512 by default but can be adjusted as needed. After resizing (if applicable), the image processor decomposes the images into square patches based on the max_image_size parameter.

This model was contributed by orrzohar.

Usage example

Single Media inference

The model can accept both images and videos as input, but you should use only one of the modalities at a time. Here's an example code for that.

from transformers import AutoModelForImageTextToText, AutoProcessor


processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
model = AutoModelForImageTextToText.from_pretrained(
    "HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
    device_map="auto"
)

conversation = [
    {
        "role": "user",
        "content":[
            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
            {"type": "text", "text": "Describe this image."}
        ]
    }
]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output_ids = model.generate(**inputs, max_new_tokens=128)
generated_texts = processor.batch_decode(output_ids, skip_special_tokens=True)
print(generated_texts)


# Video
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "video", "path": "/path/to/video.mp4"},
            {"type": "text", "text": "Describe this video in detail"}
        ]
    },
]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts[0])

Batch Mixed Media Inference

The model can batch inputs composed of several images/videos and text. Here is an example.

from transformers import AutoModelForImageTextToText, AutoProcessor


processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-256M-Video-Instruct")
model = AutoModelForImageTextToText.from_pretrained(
    "HuggingFaceTB/SmolVLM2-256M-Video-Instruct",
    device_map="auto"
)

# Conversation for the first image
conversation1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "path": "/path/to/image.jpg"},
            {"type": "text", "text": "Describe this image."}
        ]
    }
]

# Conversation with two images
conversation2 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "path": "/path/to/image.jpg"},
            {"type": "image", "path": "/path/to/image.jpg"},
            {"type": "text", "text": "What is written in the pictures?"}
        ]
    }
]

# Conversation with pure text
conversation3 = [
    {"role": "user","content": "who are you?"}
]


conversations = [conversation1, conversation2, conversation3]
inputs = processor.apply_chat_template(
    conversations,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=100)
generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_texts[0])

SmolVLMConfig

autodoc SmolVLMConfig

SmolVLMVisionConfig

autodoc SmolVLMVisionConfig

Idefics3VisionTransformer

autodoc SmolVLMVisionTransformer

SmolVLMModel

autodoc SmolVLMModel - forward - get_image_features

SmolVLMForConditionalGeneration

autodoc SmolVLMForConditionalGeneration - forward - get_image_features

SmolVLMImageProcessor

autodoc SmolVLMImageProcessor - preprocess

SmolVLMImageProcessorPil

autodoc SmolVLMImageProcessorPil - preprocess

SmolVLMVideoProcessor

autodoc SmolVLMVideoProcessor - preprocess

SmolVLMProcessor

autodoc SmolVLMProcessor - call