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---
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 |
|