|
--- |
|
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." |
|
--- |
|
<img src="Captions.jpg"> |
|
|
|
## 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 |
|
|
|
perception reasoning OCR artwork celebrity code_reasoning color commonsense_reasoning count existence landmark numerical_calculation position posters scene text_translation |
|
|
|
MME Benchmark |
|
| Model | perception| 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 | |
|
|
|
|
|
|
|
|
|
|
