metadata

library_name: transformers
tags:
  - image-captioning
  - visual-question-answering
license: apache-2.0
datasets:
  - X2FD/LVIS-Instruct4V
  - BAAI/SVIT
  - HuggingFaceH4/ultrachat_200k
language:
  - en
pipeline_tag: image-to-text
widget:
  - src: interior.jpg
    example_title: Detailed caption
    output:
      text: >-
        The image showcases a serene and well-lit bedroom. Dominating the scene
        is a bed, neatly made with a white blanket and a black headboard.
        Adjacent to the bed, a dresser stands tall, hosting a mirror, a vase,
        and a flower arrangement. A chair is positioned near the dresser,
        offering a comfortable spot to sit and relax. The room is adorned with a
        large window that offers a picturesque view of trees outside. The walls
        are painted in a soothing shade of white, enhancing the overall ambiance
        of the space.
  - src: cat.jpg
    example_title: Short caption
    output:
      text: >-
        A white and orange cat stands on its hind legs, reaching towards a
        wooden table with a white teapot and a basket of red berries. The table
        is set on a wooden bench, surrounded by orange flowers. The cat's
        position and actions suggest curiosity and playfulness.

UForm

Pocket-Sized Multimodal AI
For Content Understanding and Generation

Description

UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two parts:

CLIP-like ViT-H/14
Qwen1.5-0.5B-Chat

The model was pre-trained on the internal image captioning dataset and fine-tuned on public instructions datasets: SVIT, LVIS, VQAs datasets.

Usage

The generative model can be used to caption images, answer questions about them. Also it is suitable for a multimodal chat.

from transformers import AutoModel, AutoProcessor

model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-halfB", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-halfB", trust_remote_code=True)

prompt = "Question or Instruction"
image = Image.open("image.jpg")

inputs = processor(text=[prompt], images=[image], return_tensors="pt")
with torch.inference_mode():
     output = model.generate(
        **inputs,
        do_sample=False,
        use_cache=True,
        max_new_tokens=256,
        eos_token_id=151645,
        pad_token_id=processor.tokenizer.pad_token_id
    )

prompt_len = inputs["input_ids"].shape[1]
decoded_text = processor.batch_decode(output[:, prompt_len:])[0]

You can check examples of different prompts in our demo space.

Evaluation

For captioning evaluation we measure CLIPScore and RefCLIPScore¹.

Model	LLM Size	SQA	MME	MMBench	Average¹
UForm-Gen2-Qwen-halfB	0.5B	45.5	880.1	42.0	29.31
MobileVLM v2	1.4B	52.1	1302.8	57.7	36.81
LLaVA-Phi	2.7B	68.4	1335.1	59.8	42.95

¹MME scores were divided by 2000 before averaging.

UForm

Pocket-Sized Multimodal AI For Content Understanding and Generation

Description

Usage

Evaluation

Pocket-Sized Multimodal AI
For Content Understanding and Generation