Update README.md

README.md

@@ -1,3 +1,92 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Lin-Chen/open-llava-next-llama3-8b
+tags:
+- remote sensing
+---
+# 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/edit/main/README.md)
+
+## 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}
+}
+```