Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- vision
+- image-captioning
+pipeline_tag: image-to-text
+---
+
+# InstructBLIP model
+
+InstructBLIP model using [Vicuna-7b](https://github.com/lm-sys/FastChat#model-weights) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al.
+
+Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details.
+
+![InstructBLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg)
+
+## Intended uses & limitations
+
+Usage is as follows:
+
+```
+from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
+import torch
+from PIL import Image
+import requests
+
+model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b")
+processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+prompt = "What is unusual about this image?"
+inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+outputs = model.generate(
+        **inputs,
+        do_sample=False,
+        num_beams=1,
+        max_length=256,
+        min_length=1,
+        top_p=0.9,
+        repetition_penalty=1.5,
+        length_penalty=1.0,
+        temperature=1,
+)
+generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
+print(generated_text)
+```
+
+Note that this shows unconditional generation of text given an image. You can also make the model continue a text prompt.
+
+### How to use
+
+For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).