Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: mit
----
+---
+license: mit
+---
+# Two steps only need.
+
+First step. (git clone and install required packages)
+```bash
+# Download Project Code
+git clone https://github.com/ByungKwanLee/Phantom
+
+# Virtual Environment
+conda create -n trol python=3.11 -y
+conda activate trol
+
+# install torch
+pip3 install torch torchvision
+
+# install requiresments
+pip install -r requirements.txt
+
+# flash attention
+pip install flash-attn --no-build-isolation
+
+# all cache deleted 
+conda clean -a && pip cache purge
+```
+
+Second step. (open, edit, and run `demo.py`)
+```python
+# model selection
+size = '1.8b' # [Select One] '0.5b' (transformers more recent version) | '1.8b' | '3.8b' (transformers==4.37.2) | '7b'
+
+# User prompt
+prompt_type="with_image" # Select one option "text_only", "with_image"
+img_path='figures/demo.png'
+question="Describe the image in detail"
+
+# loading model
+model, tokenizer = load_model(size=size)
+
+# prompt type -> input prompt
+if prompt_type == 'with_image':
+    # Image Load
+    image = pil_to_tensor(Image.open(img_path).convert("RGB"))
+    inputs = [{'image': image, 'question': question}]
+elif prompt_type=='text_only':
+    inputs = [{'question': question}]
+
+# cpu -> gpu
+for param in model.parameters():
+    if not param.is_cuda:
+        param.data = param.cuda()
+
+# Generate
+with torch.inference_mode():
+
+    # Model
+    _inputs = model.eval_process(inputs=inputs,
+                                data='demo',
+                                tokenizer=tokenizer,
+                                device='cuda:0')
+    generate_ids = model.generate(**_inputs, do_sample=False, max_new_tokens=256)
+answer = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]
+print(answer)
+```
+
+So easy to run the code Let's shout Phantom!