Update README.md

README.md

@@ -1,3 +1,91 @@
----
-license: llama3.2
----
+---
+license: llama3.2
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-11B-Vision-Instruct
+tags:
+- food
+- recipe
+---
+# Adapting Multimodal Large Language Models to Domains via Post-Training
+
+This repos contains the **food MLLM developed from Llama-3.2-11B** 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)
+
+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='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png" width="1000">
+</p>
+
+## How to use
+
+Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+```bash
+import requests
+import torch
+from PIL import Image
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+
+model_id = "AdaptLLM/medicine-Llama-3.2-11B-Vision-Instruct"
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": "If I had to write a haiku for this one, it would be: "}
+    ]}
+]
+input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    image,
+    input_text,
+    add_special_tokens=False,
+    return_tensors="pt"
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=30)
+print(processor.decode(output[0]))
+```
+
+## 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}
+}
+```
+
+[AdaptLLM](https://huggingface.co/papers/2309.09530) (ICLR 2024)
+```bibtex
+@inproceedings{
+adaptllm,
+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}
+}
+```