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:
- CLIP-like ViT-H/14
- 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 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.
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 |