Update README.md

README.md

@@ -1,3 +1,94 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- AdaptLLM/medicine-visual-instructions
+language:
+- en
+base_model:
+- Lin-Chen/open-llava-next-llama3-8b
+tags:
+- medical
+- biology
+---
+# Adapting Multimodal Large Language Models to Domains via Post-Training
+
+This repo contains the **biomedicine 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 correspoding training dataset is in [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions).
+
+The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains/edit/main/README.md)
+
+We investigate domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. 
+**(1) Data Synthesis**: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. **Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs.** 
+**(2) Training Pipeline**: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. 
+**(3) Task Evaluation**: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.
+
+<p align='center'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
+</p>
+
+## How to use
+
+```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/medicine-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)
+```
+
+## Citation
+If you find our work helpful, please cite us.
+
+AdaMLLM
+```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}
+}
+```
+
+[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
+```bibtex
+@article{cheng2024instruction,
+  title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
+  author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
+  journal={arXiv preprint arXiv:2406.14491},
+  year={2024}
+}
+```