Update README.md

README.md

@@ -1,8 +1,187 @@
 ---
 license: apache-2.0
+datasets:
+- FreedomIntelligence/ALLaVA-4V
+- Vision-Flan/vision-flan_191-task_1k
+language:
+- en
+base_model:
+- Lin-Chen/open-llava-next-llama3-8b
 ---
+# Adapting Multimodal Large Language Models to Domains via Post-Training
+
+This repos contains the **visual-instruction synthesizer** 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/eut1QtM5vi7tZyD5zvlr7.jpeg" width="600">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png" width="1000">
 </p>
 
+## How to use
+To synthesize an "instruction-informative response-precise response" triplet based on the following image-caption pair.
+
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/mgI_Ayj12_Q_kviWvfAVb.jpeg" width="300">
+</p>
+
+```python
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+import torch
+from PIL import Image
+import requests
+
+# Define your input image-caption pair here:
+## image 
+url = "https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/mgI_Ayj12_Q_kviWvfAVb.jpeg"
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+## Caption
+caption = "Dish: Strawberry Waffles\n\nSteps to prepare:\na). Preheat and grease a waffle iron according to manufacturer's instructions.\nb). Sift flour, baking powder, and salt together in a bowl. Whisk buttermilk, yogurt, butter, eggs, and sugar together in a separate bowl; stir into flour mixture until batter is smooth. Fold strawberries into batter.\nc). Pour about 1/3 cup batter into preheated waffle iron; cook until lightly browned, 5 to 7 minutes. Repeat with remaining batter.\n\nIngredients you'll need:\n(a). 2 1/2 cups all-purpose flour\n(b). 4 teaspoons baking powder\n(c). 3/4 teaspoon salt\n(d). 2 cups buttermilk\n(e). 1/2 cup vanilla Greek-style yogurt\n(f). 1/2 cup butter, melted\n(g). 2 eggs, beaten\n(h). 1 1/2 tablespoons white sugar\n(i). 3/4 cup chopped strawberries, or more to taste"
+
+# Path to synthesizer
+model_path = "AdaptLLM/visual-instruction-synthesizer"
+
+
+# =========================== Do NOT need to modify the following ===============================
+
+# Prompt Hints
+caption_hint = "Describe the image."
+precise_hint = "Answer with a precise response.\n"
+informative_hint = "Answer with an informative response.\n"
+
+# Function to parse predictions
+def parse_pred(pred):
+    if not pred.endswith("<|end_of_text|>"):
+        return []
+
+    pred = pred[:-len("<|end_of_text|>")]
+
+    QA_str_list = pred.split("<|start_header_id|>user<|end_header_id|>\n\n")
+    if not pred.endswith("<|eot_id|>"):
+        QA_str_list = QA_str_list[:-1]
+
+    QA_list = []
+    for QA_str in QA_str_list:
+        try:
+            assert "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" in QA_str
+            Q_str, A_str = QA_str.split("<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n")
+            Q_str, A_str = Q_str.strip(), A_str[:-len("<|eot_id|>")].strip()
+            assert Q_str and A_str
+            QA_list.append({"Q": Q_str, "A": A_str})
+        except AssertionError:
+            pass  # Skip invalid entries
+
+    conversations = []
+    for qa_entry in QA_list:
+        conversations.append({"from": "human", "value": qa_entry["Q"]})
+        conversations.append({"from": "gpt", "value": qa_entry["A"]})
+    return conversations
+
+# Function to extract task triplets
+def get_task_triplet(pred):
+    pred_QAs = parse_pred(pred)
+    precise_QAs = {}
+    informative_QAs = {}
+    collected_QA = None
+
+    for idx in range(0, len(pred_QAs), 2):  # Iterate over question-answer pairs
+        question = pred_QAs[idx]["value"]
+        answer = pred_QAs[idx + 1]["value"]
+        if question.startswith(precise_hint):
+            precise_q = question[len(precise_hint):]
+            if precise_q in informative_QAs:
+                collected_QA = {
+                    "Q": precise_q,
+                    "precise_A": answer,
+                    "informative_A": informative_QAs[precise_q],
+                }
+                break
+            else:
+                precise_QAs[precise_q] = answer
+        elif question.startswith(informative_hint):
+            informative_q = question[len(informative_hint):]
+            if informative_q in precise_QAs:
+                collected_QA = {
+                    "Q": informative_q,
+                    "precise_A": precise_QAs[informative_q],
+                    "informative_A": answer,
+                }
+                break
+            else:
+                informative_QAs[informative_q] = answer
+
+    return collected_QA
+
+# 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{caption_hint}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    f"{caption}<|eot_id|><|start_header_id|>user<|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=False)
+print(f"## Synthesizer predictions:\n{pred}")
+
+# Extract task triplets
+task_triplet = get_task_triplet(pred)
+print(f"## Synthesized Task triplet:\n{task_triplet}")
+```
+
+
+## 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}
+}
+```
+
+[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}
+}
+```