Create README.md

README.md

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+<h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
+
+[GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/tree/main)
+
+
+
+## Usage
+Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
+```
+torch==2.0.1
+torchvision==0.15.2
+transformers==4.37.2
+megfile==3.1.2
+```
+
+
+```python
+# test.py
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
+    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
+
+image = Image.open('xx.jpg').convert('RGB')
+question = 'What is in the image?'
+msgs = [{'role': 'user', 'content': [image, question]}]
+
+res = model.chat(
+    image=None,
+    msgs=msgs,
+    tokenizer=tokenizer
+)
+print(res)
+
+## if you want to use streaming, please make sure sampling=True and stream=True
+## the model.chat will return a generator
+res = model.chat(
+    image=None,
+    msgs=msgs,
+    tokenizer=tokenizer,
+    sampling=True,
+    stream=True
+)
+
+generated_text = ""
+for new_text in res:
+    generated_text += new_text
+    print(new_text, flush=True, end='')
+```