---
license: mit
pipeline_tag: image-text-to-text
---

## CoLLaVO model

This repository contains the weights of the model presented in [CoLLaVO: Crayon Large Language and Vision mOdel](https://huggingface.co/papers/2402.11248).

### Simple running code is based on [MoAI-Github](https://github.com/ByungKwanLee/MoAI).

You need only the following seven steps.

### Simple running code is based on [CoLLaVO-Github](https://github.com/ByungKwanLee/CoLLaVO).

You need only the following seven steps.

### [0] Download Github Code of CoLLaVO, install the required libraries, set the necessary environment variable (README.md explains in detail! Don't Worry!).

```bash
git clone https://github.com/ByungKwanLee/CoLLaVO
bash install
```

### [1] Loading Image

```python
from PIL import Image
from torchvision.transforms import Resize
from torchvision.transforms.functional import pil_to_tensor
image_path = "figures/crayon_image.jpg"
image = Resize(size=(490, 490), antialias=False)(pil_to_tensor(Image.open(image_path)))
```

### [2] Instruction Prompt

```python
prompt = "Describe this image in detail."
```

### [3] Loading CoLlaVO
```python
from collavo.load_collavo import prepare_collavo
collavo_model, collavo_processor, seg_model, seg_processor = prepare_collavo(collavo_path='BK-Lee/CoLLaVO-7B', bits=4, dtype='fp16')

```

### [4] Pre-processing for CoLLaVO
```python
collavo_inputs = collavo_model.demo_process(image=image, 
                                    prompt=prompt, 
                                    processor=collavo_processor,
                                    seg_model=seg_model,
                                    seg_processor=seg_processor,
                                    device='cuda:0')
```

### [5] Generate
```python
import torch
with torch.inference_mode():
    generate_ids = collavo_model.generate(**collavo_inputs, do_sample=True, temperature=0.9, top_p=0.95, max_new_tokens=256, use_cache=True)
```

### [6] Decoding
```python
answer = collavo_processor.batch_decode(generate_ids, skip_special_tokens=True)[0].split('[U')[0]
print(answer)
```