Image-to-Text
Transformers
Safetensors
English
vlm
feature-extraction
image-captioning
visual-question-answering
custom_code
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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

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
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