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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
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."
---
<h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
</h3>
## 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. CLIP-like ViT-H/14
2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/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.
The model took 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-qwen-500m", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", 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
| Model | LLM Size | SQA | MME | MMBench | Average¹ |
| :---------------------------------- | -------: | -----:| ------:| --------:| --------:|
| UForm-Gen2-Qwen-500m | 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.
|