---
license: apache-2.0
language:
- en
base_model:
- Lin-Chen/open-llava-next-llama3-8b
tags:
- remote-sensing
datasets:
- AdaptLLM/remote-sensing-visual-instructions
---
# Adapting Multimodal Large Language Models to Domains via Post-Training

This repo contains the **remote sensing MLLM developed from LLaVA-NeXT-Llama3-8B** in our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930).

The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)

## 1. To Chat with AdaMLLM  

```python
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
import torch
from PIL import Image
import requests

# Define your input image and instruction here:
## image 
url = "https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

instruction = "What's in the image?"

model_path='AdaptLLM/remote-sensing-LLaVA-NeXT-Llama3-8B'

# =========================== Do NOT need to modify the following ===============================
# Load the processor
processor = LlavaNextProcessor.from_pretrained(model_path)

# Define image token
image_token = "<|reserved_special_token_4|>"

# Format the prompt
prompt = (
    f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
    f"You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
    f"<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
    f"{image_token}\n{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
)

# Load the model
model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

# Prepare inputs and generate output
inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
answer_start = int(inputs["input_ids"].shape[-1])
output = model.generate(**inputs, max_new_tokens=512)

# Decode predictions
pred = processor.decode(output[0][answer_start:], skip_special_tokens=True)
print(pred)
```

## 2. To Evaluate Any MLLM on Domain-Specific Benchmarks  

Refer to the [remote-sensing-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/remote-sensing-VQA-benchmark) to reproduce our results and evaluate many other MLLMs on domain-specific benchmarks.  

## 3. To Reproduce this Domain-Adapted MLLM  

See [Post-Train Guide](https://github.com/bigai-ai/QA-Synthesizer/blob/main/docs/Post_Train.md) to adapt MLLMs to domains.

## Citation
If you find our work helpful, please cite us.

[AdaMLLM](https://huggingface.co/papers/2411.19930)
```bibtex
@article{adamllm,
  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
  journal={arXiv preprint arXiv:2411.19930},
  year={2024}
}
```

[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
```