Create README.md

README.md

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+---
+license: mit
+---
+
+You should follow the two steps
+
+1. Install libraries and dowloand github package [Meteor](https://github.com/ByungKwanLee/Meteor)
+```bash
+bash install
+conda activate meteor
+pip install -r requirements.txt
+```
+
+2. Run the file: demo.py in [Meteor](https://github.com/ByungKwanLee/Meteor)
+
+You can choose prompt type: text_only or with_image!
+Enjoy Meteor!
+
+
+```python
+import time
+import torch
+from config import *
+from PIL import Image
+from utils.utils import *
+import torch.nn.functional as F
+from meteor.load_mmamba import load_mmamba
+from meteor.load_meteor import load_meteor
+from torchvision.transforms.functional import pil_to_tensor
+
+# User prompt
+prompt_type='with_image' # text_only / with_image
+img_path='figures/demo.png'
+question='Provide the detail of the image'
+
+# loading meteor model
+mmamba = load_mmamba('BK-Lee/Meteor-Mamba').cuda()
+meteor, tok_meteor = load_meteor('BK-Lee/Meteor-MLM', bits=4)
+
+# freeze model
+freeze_model(mmamba)
+freeze_model(meteor)
+
+# Device
+device = torch.cuda.current_device()
+
+# prompt type -> input prompt
+image_token_number = int((490/14)**2)
+if prompt_type == 'with_image':
+    # Image Load
+    image = F.interpolate(pil_to_tensor(Image.open(img_path).convert("RGB")).unsqueeze(0), size=(490, 490), mode='bicubic').squeeze(0)
+    inputs = [{'image': image, 'question': question}]
+elif prompt_type=='text_only':
+    inputs = [{'question': question}]
+
+# Generate
+with torch.inference_mode():
+
+    # Meteor Mamba
+    mmamba_inputs = mmamba.eval_process(inputs=inputs, tokenizer=tok_meteor, device=device, img_token_number=image_token_number)
+    if 'image' in mmamba_inputs.keys():
+        clip_features = meteor.clip_features(mmamba_inputs['image'])
+        mmamba_inputs.update({"image_features": clip_features})
+    mmamba_outputs = mmamba(**mmamba_inputs)
+    
+    # Meteor
+    meteor_inputs = meteor.eval_process(inputs=inputs, data='demo', tokenizer=tok_meteor, device=device, img_token_number=image_token_number)
+    if 'image' in mmamba_inputs.keys():
+        meteor_inputs.update({"image_features": clip_features})
+    meteor_inputs.update({"tor_features": mmamba_outputs.tor_features})
+
+    # Generation
+    generate_ids = meteor.generate(**meteor_inputs, do_sample=True, max_new_tokens=128, top_p=0.95, temperature=0.9, use_cache=True)
+
+# Text decoding
+decoded_text = tok_meteor.batch_decode(generate_ids, skip_special_tokens=True)[0].split('assistant\n')[-1].split('[U')[0].strip()
+print(decoded_text)
+```