Add: README.md

README.md

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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
+- MMInstruction/VLFeedback
+- zhiqings/LLaVA-Human-Preference-10K
+language:
+- en
+pipeline_tag: image-to-text
+widget:
+  - src: interior.jpg
+    example_title: Detailed caption
+    output:
+      text: "The image shows a serene and well-lit bedroom with a white bed, a black bed frame, and a white comforter. There’s a gray armchair with a white cushion, a black dresser with a mirror and a vase, and a white rug on the floor. The room has a large window with white curtains, and there are several decorative items, including a picture frame, a vase with a flower, and a lamp. The room is well-organized and has a calming atmosphere."
+  - 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 raspberries. The table is on a small wooden bench, surrounded by orange flowers. The cat’s position and action create a serene, playful scene in a garden."
 ---
+<h1 align="center">UForm</h1>
+<h3 align="center">
+Pocket-Sized Multimodal AI<br/>
+For Content Understanding and Generation<br/>
+</h3>
+
+## 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](https://huggingface.co/Qwen/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](https://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.
+
+```python
+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
+
+MME Benchmark
+| Model                               | 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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