Update README.md

README.md

@@ -1,3 +1,63 @@
----
-license: llama3.2
----
+---
+license: llama3.2
+---
+
+## Model Information
+
+Llama-3.2-11B-Vision-Instruct-Swallow-8B-Merge was created using Chat Vector to add Japanese language capability to Meta/Llama-3.2-11B-Vision-Instruct.
+
+Llama-3.2-11B-Vision-Instruct-Swallow-8B-Mergeは、Meta/Llama-3.2-11B-Vision-Instructに日本語能力を付加するためにChat Vectorを用いて作成されました。
+
+### Detail
+
+https://zenn.dev/kendama/articles/280a4089cb8a72
+
+## Recipe
+```
+Llama-3.2-11B-Vision-Instruct + (Llama-3.1-Swallow-8B-v0.1 - Llama-3.1-8B)
+```
+- Vision Model: [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
+- Base Text Model: [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
+- Japanese Text Model: [tokyotech-llm/Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1)
+
+## License
+
+[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+
+## How to use
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+
+model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": "この画像で一句詠んでください。"}
+    ]}
+]
+input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    image,
+    input_text,
+    add_special_tokens=False,
+    return_tensors="pt"
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=30)
+print(processor.decode(output[0]))
+```