huggingface/transformers v4.49.0-SmolVLM-2

SmolVLM-2 (Based on v4.49.0)

on GitHub

A new model is added to transformers: SmolVLM-2.
It is added on top of the v4.49.0 release, and can be installed from the following tag: v4.49.0-SmolVLM-2.

In order to install this version, please install with the following command:

pip install git+https://github.com/huggingface/transformers@v4.49.0-SmolVLM-2

If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.

SmolVLM-2

SmolVLM-2 is detailed in the following blog post.

The models and demos using the model are available in the following collection.

Overview

SmolVLM2 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.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

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

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, dtype=torch.bfloat16)

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, dtype=torch.bfloat16)

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])