---
library_name: transformers
tags:
- image-captioning
- visual-question-answering
license: apache-2.0
datasets:
- X2FD/LVIS-Instruct4V
- BAAI/SVIT
- HuggingFaceH4/ultrachat_200k
- MMInstruction/VLFeedback
- zhiqings/LLaVA-Human-Preference-10K
language:
- en
pipeline_tag: image-to-text
widget:
  - src: interior.jpg
    example_title: Detailed caption
    output:
      text: "The image shows a serene and well-lit bedroom with a white bed, a black bed frame, and a white comforter. There’s a gray armchair with a white cushion, a black dresser with a mirror and a vase, and a white rug on the floor. The room has a large window with white curtains, and there are several decorative items, including a picture frame, a vase with a flower, and a lamp. The room is well-organized and has a calming atmosphere."
  - 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 raspberries. The table is on a small wooden bench, surrounded by orange flowers. The cat’s position and action create a serene, playful scene in a garden."
---
<h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
</h3>

## Description 

UForm-Gen2-dpo is a small generative vision-language model alined for Image Captioning and Visual Question Answering 
on preference datasets VLFeedback and LLaVA-Human-Preference-10K using Direct Preference Optimization (DPO). 

The model consists of two parts: 
1. CLIP-like ViT-H/14
2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)

The model took less than one day to train on a DGX-H100 with 8x H100 GPUs.
Thanks to [Nebius.ai](https://nebius.ai) for providing the compute 🤗

### Usage


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

```python
from transformers import AutoModel, AutoProcessor
model = AutoModel.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-dpo", 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

MME Benchmark
| Model                               | reasoning |  OCR  |  artwork   | celebrity  | code_reasoning | color | commonsense_reasoning | count | existence | landmark | numerical_calculation | position | posters | scene | text_translation |
| :---------------------------------- | --------: | -----:| ----------:| ----------:| --------------:| -----:| ---------------------:| -----:| ---------:| --------:| ---------------------:| --------:| -------:| -----:| ----------------:|
| uform-gen2-dpo | 1,048.75 |	224.64 |	72.50 |	97.25 |	62.65 |	67.50 |	123.33 |	57.14 |	136.67 |	195.00 |	104.00 |	50.00 |	51.67 |	59.18 |	146.50 |	50.00 |
| uform-gen2-qwen-500m | 863.40 |	236.43 |	57.50 |	93.00 |	67.06 |	57.50 |	78.33 |	81.43 |	53.33 |	150.00 |	98.00 |	50.00 |	50.00 |	62.93 |	153.25 |	47.50 |