Update README.md

README.md

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 license: apache-2.0
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+# Mocha Checkpoint for BLIP-Large Model
+
+The official checkpoint of BLIP-Large model, finetuned on MS-COCO with the MOCHa RL frameword, introduced in [MOCHa: Multi-Objective Reinforcement Mitigating Caption Hallucinations](https://arxiv.org/pdf/2312.03631.pdf)
+
+[Project Page](https://assafbk.github.io/mocha/)
+
+## Usage
+
+You can use this model for conditional and un-conditional image captioning
+
+### Using the Pytorch model
+
+#### Running the model on CPU
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
+model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+```
+</details>
+
+#### Running the model on GPU
+
+##### In full precision 
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
+model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha").to("cuda")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+```
+</details>
+
+##### In half precision (`float16`)
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("moranyanuka/blip-image-captioning-large-mocha")
+model = BlipForConditionalGeneration.from_pretrained("moranyanuka/blip-image-captioning-large-mocha", torch_dtype=torch.float16).to("cuda")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+# >>> a photography of a woman and her dog
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach with her dog
+```
+</details>
+
+bibtex:
+```
+@misc{benkish2023mocha,
+      title={MOCHa: Multi-Objective Reinforcement Mitigating Caption Hallucinations}, 
+      author={Assaf Ben-Kish and Moran Yanuka and Morris Alper and Raja Giryes and Hadar Averbuch-Elor},
+      year={2023},
+      eprint={2312.03631},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+      }