UForm 2 GenAI
Collection
Miniature multimodal Vision-Language Models
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4 items
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Updated
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UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two parts:
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 for providing the compute 🤗
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-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.
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.