Hugging Face's logo

unum-cloud
/
uform-gen-chat

Visual Question Answering
Transformers
Safetensors
English
vlm
text-generation
image-captioning
Inference Endpoints
Model card Files Community
uform-gen-chat / README.md
ashvardanian's picture
ashvardanian
Update README.md
409c1bb
preview code
|
raw history blame contribute delete
3.01 kB
metadata
pipeline_tag: visual-question-answering
tags:
  - image-captioning
  - visual-question-answering
datasets:
  - sbu_captions
  - visual_genome
  - HuggingFaceM4/VQAv2
  - ChristophSchuhmann/MS_COCO_2017_URL_TEXT
language:
  - en
license: apache-2.0
base_model: unum-cloud/uform-vl-english

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:

  1. UForm Vision Encoder
  2. Sheared-LLaMA-1.3B manually tuned on the instructions dataset

The model was pre-trained on: MSCOCO, SBU Captions, Visual Genome, VQAv2, GQA and a few internal datasets. UForm-Gen-Chat is SFT version of UForm-Gen for multimodal chat.

Usage 

pip install uform

For the CLI demo run the following:

uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg
uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg --device="cuda:0" --fp16

Or if you want to use the model in your code:

from uform.gen_model import VLMForCausalLM, VLMProcessor

model = VLMForCausalLM.from_pretrained("unum-cloud/uform-gen-chat")
processor = VLMProcessor.from_pretrained("unum-cloud/uform-gen-chat")

prompt = "What do you see?"
image = Image.open("zebra.jpg")

inputs = processor(texts=[prompt], images=[image], return_tensors="pt")
with torch.inference_mode():
     output = model.generate(
        **inputs,
        do_sample=False,
        use_cache=True,
        max_new_tokens=128,
        eos_token_id=32001,
        pad_token_id=processor.tokenizer.pad_token_id
    )

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

Evaluation

For captioning evaluation we measure CLIPScore and RefCLIPScore¹.

Model Size Caption Length CLIPScore RefCLIPScore
llava-hf/llava-1.5-7b-hf 7B Long 0.878 0.529
llava-hf/llava-1.5-7b-hf 7B Short 0.886 0.531
Salesforce/instructblip-vicuna-7b 7B Long 0.902 0.534
Salesforce/instructblip-vicuna-7b 7B Short 0.848 0.523
unum-cloud/uform-gen-chat 1.5B Long 0.860 0.525
unum-cloud/uform-gen-chat 1.5B Short 0.858 0.525

¹ We used apple/DFN5B-CLIP-ViT-H-14-378 CLIP model.