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--- |
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pipeline_tag: visual-question-answering |
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tags: |
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- image-captioning |
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- visual-question-answering |
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datasets: |
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- sbu_captions |
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- visual_genome |
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- HuggingFaceM4/VQAv2 |
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- ChristophSchuhmann/MS_COCO_2017_URL_TEXT |
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language: |
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- en |
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license: apache-2.0 |
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base_model: unum-cloud/uform-vl-english |
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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. [UForm Vision Encoder](https://huggingface.co/unum-cloud/uform-vl-english) |
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2. [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) manually tuned on the instructions dataset |
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The model was pre-trained on: MSCOCO, SBU Captions, Visual Genome, VQAv2, GQA and a few internal datasets. UForm-Gen-Chat is SFT version of [`UForm-Gen`](https://huggingface.co/unum-cloud/uform-gen) for multimodal chat. |
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### Usage |
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```bash |
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pip install uform |
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``` |
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For the CLI demo run the following: |
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```bash |
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uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg |
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uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg --device="cuda:0" --fp16 |
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``` |
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Or if you want to use the model in your code: |
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```python |
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from uform.gen_model import VLMForCausalLM, VLMProcessor |
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model = VLMForCausalLM.from_pretrained("unum-cloud/uform-gen-chat") |
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processor = VLMProcessor.from_pretrained("unum-cloud/uform-gen-chat") |
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prompt = "What do you see?" |
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image = Image.open("zebra.jpg") |
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inputs = processor(texts=[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=128, |
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eos_token_id=32001, |
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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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## Evaluation |
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For captioning evaluation we measure CLIPScore and RefCLIPScore¹. |
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| Model | Size | Caption Length | CLIPScore | RefCLIPScore | |
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| :---------------------------------- | ---: | -------------: | --------: | -----------: | |
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| `llava-hf/llava-1.5-7b-hf` | 7B | Long | 0.878 | 0.529 | |
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| `llava-hf/llava-1.5-7b-hf` | 7B | Short | 0.886 | 0.531 | |
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| `Salesforce/instructblip-vicuna-7b` | 7B | Long | 0.902 | 0.534 | |
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| `Salesforce/instructblip-vicuna-7b` | 7B | Short | 0.848 | 0.523 | |
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| `unum-cloud/uform-gen-chat` | 1.5B | Long | 0.860 | 0.525 | |
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| `unum-cloud/uform-gen-chat` | 1.5B | Short | 0.858 | 0.525 | |
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¹ We used `apple/DFN5B-CLIP-ViT-H-14-378` CLIP model. |
