unum-cloud
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uform-gen2-qwen-500m

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- image-captioning
+- visual-question-answering
+license: apache-2.0
+datasets:
+- X2FD/LVIS-Instruct4V
+- BAAI/SVIT
+- HuggingFaceH4/ultrachat_200k
+language:
+- en
+pipeline_tag: image-to-text
+widget:
+  - src: interior.jpg
+    example_title: Detailed caption
+    output:
+      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."
+  - src: cat.jpg
+    example_title: Short caption
+    output:
+      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."
 ---
+<h1 align="center">UForm</h1>
+<h3 align="center">
+Pocket-Sized Multimodal AI<br/>
+For Content Understanding and Generation<br/>
+</h3>
+## Description
+UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two parts:
+1. CLIP-like ViT-H/14
+2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)
+The model was pre-trained on the internal image captioning dataset and fine-tuned on public instructions datasets: SVIT, LVIS, VQAs datasets.
+### Usage
+The generative model can be used to caption images, answer questions about them. Also it is suitable for a multimodal chat.
+```python
+from transformers import AutoModel, AutoProcessor
+model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-halfB", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-halfB", 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
+For captioning evaluation we measure CLIPScore and RefCLIPScore¹.
+| Model                               | LLM Size |  SQA  |  MME   | MMBench  | Average¹ |
+| :---------------------------------- | -------: | -----:| ------:| --------:| --------:|
+| UForm-Gen2-Qwen-halfB               |   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.