README.md

---
base_model:
- tokyotech-llm/Llama-3.1-Swallow-8B-v0.1
- meta-llama/Llama-3.2-11B-Vision-Instruct
- meta-llama/Llama-3.1-8B
license: llama3.2
tags:
- merge
---

## 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 = "Kendamarron/Llama-3.2-11B-Vision-Instruct-Swallow-8B-Merge"

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]))
```