---
base_model:
- MAmmoTH-VL/MAmmoTH-VL-8B
datasets:
- TIGER-Lab/VisualWebInstruct
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
---

# Introduction
MAmmoTH-VL2, the model trained with VisualWebInstruct.

# Links
[Github](https://github.com/TIGER-AI-Lab/VisualWebInstruct)|
[Paper](https://arxiv.org/abs/2503.10582)|
[Website](https://tiger-ai-lab.github.io/VisualWebInstruct/)




# Example Usage
## Requirements
```python
llava==1.7.0.dev0 # pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
torch==2.5.1
```
To perform inference using MAmmoTH-VL2, you can use the following code snippet:
```python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import process_images
from llava.constants import DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates

from PIL import Image
import requests
import copy
import torch

# Load MAmmoTH-VL2 model
pretrained = "TIGER-Lab/MAmmoTH-VL2"
model_name = "llava_qwen"
device = "cuda:3"  # Specify a single GPU
device_map = {"": device}

# Load model
tokenizer, model, image_processor, max_length = load_pretrained_model(
    pretrained, 
    None, 
    model_name, 
    device_map=device_map,
    multimodal=True
)
model.eval()
model = model.to(device)

# Load image
image_url = "https://raw.githubusercontent.com/jymmmmm/VISUALWEBINSTRUCT/main/image.png"
image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
images = [image]
image_sizes = [[image.size[0], image.size[1]]]

# Prepare prompt
prompt = "In the picture shown below, prove ΔWXY and ΔZWY are similar. Please conclude your answer as Answer: xxx at the end if possible."

# Set up conversation template
try:
    conv_template = "qwen_2_5"
    conv = copy.deepcopy(conv_templates[conv_template])
except KeyError:
    available_templates = list(conv_templates.keys())
    for template_name in available_templates:
        if 'qwen' in template_name.lower():
            conv_template = template_name
            break
    else:
        conv_template = available_templates[0]
    conv = copy.deepcopy(conv_templates[conv_template])

# Add question with image
question = DEFAULT_IMAGE_TOKEN + "\n" + prompt
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()

# Prepare model inputs
inputs = tokenizer(
    prompt_question, 
    return_tensors="pt",
    padding=True,
    truncation=True,
    max_length=max_length
)
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)

# Process image
image_tensor = process_images(images, image_processor, model.config)
if isinstance(image_tensor, list):
    image_tensor = [img.to(dtype=torch.float16, device=device) for img in image_tensor]
else:
    image_tensor = image_tensor.to(dtype=torch.float16, device=device)

# Generate response
with torch.no_grad():
    outputs = model.generate(
        input_ids,
        attention_mask=attention_mask,
        images=image_tensor,
        image_sizes=image_sizes,
        do_sample=False,
        temperature=0,
        max_new_tokens=512,
    )

# Decode response
input_token_len = input_ids.shape[1]
response = tokenizer.batch_decode(outputs[:, input_token_len:], skip_special_tokens=True)[0]
print("Response:", response)

```





# Citation
```
@article{visualwebinstruct,
    title={VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search},
    author = {Jia, Yiming and Li, Jiachen and Yue, Xiang and Li, Bo and Nie, Ping and Zou, Kai and Chen, Wenhu},
    journal={arXiv preprint arXiv:2503.10582},
    year={2025}
}
```