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--- |
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library_name: transformers |
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tags: |
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- image-captioning |
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- visual-question-answering |
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license: apache-2.0 |
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datasets: |
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- X2FD/LVIS-Instruct4V |
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- BAAI/SVIT |
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- HuggingFaceH4/ultrachat_200k |
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language: |
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- en |
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pipeline_tag: image-to-text |
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widget: |
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- src: interior.jpg |
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example_title: Detailed caption |
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output: |
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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." |
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- src: cat.jpg |
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example_title: Short caption |
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output: |
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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." |
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--- |
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<h1 align="center">UForm</h1> |
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<h3 align="center"> |
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Pocket-Sized Multimodal AI<br/> |
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For Content Understanding and Generation<br/> |
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</h3> |
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## Description |
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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: |
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1. CLIP-like ViT-H/14 |
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2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) |
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The model was pre-trained on the internal image captioning dataset and fine-tuned on public instructions datasets: SVIT, LVIS, VQAs datasets. |
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The model took one day to train on a DGX-H100 with 8x H100 GPUs. |
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Thanks to [Nebius.ai](https://nebius.ai) for providing the compute 🤗 |
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### Usage |
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The generative model can be used to caption images, answer questions about them. Also it is suitable for a multimodal chat. |
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```python |
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from transformers import AutoModel, AutoProcessor |
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model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True) |
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prompt = "Question or Instruction" |
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image = Image.open("image.jpg") |
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inputs = processor(text=[prompt], images=[image], return_tensors="pt") |
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with torch.inference_mode(): |
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output = model.generate( |
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**inputs, |
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do_sample=False, |
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use_cache=True, |
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max_new_tokens=256, |
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eos_token_id=151645, |
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pad_token_id=processor.tokenizer.pad_token_id |
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) |
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prompt_len = inputs["input_ids"].shape[1] |
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decoded_text = processor.batch_decode(output[:, prompt_len:])[0] |
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``` |
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You can check examples of different prompts in our demo space. |
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## Evaluation |
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| Model | LLM Size | SQA | MME | MMBench | Average¹ | |
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| :---------------------------------- | -------: | -----:| ------:| --------:| --------:| |
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| UForm-Gen2-Qwen-500m | 0.5B | 45.5 | 880.1 | 42.0 | 29.31 | |
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| MobileVLM v2 | 1.4B | 52.1 | 1302.8 | 57.7 | 36.81 | |
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| LLaVA-Phi | 2.7B | 68.4 | 1335.1 | 59.8 | 42.95 | |
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¹MME scores were divided by 2000 before averaging. |
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