Create down_openr1.py

down_openr1.py

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+import json
+from tqdm import tqdm
+from datasets import load_dataset
+import os
+
+ds_id = "lmms-lab/multimodal-open-r1-8k-verified"
+out_root = "LLaMA-Factory/data"
+dataset_name = "open_r1_v2"
+ds = load_dataset(ds_id, split="train")
+SYSTEM_PROMPT = (
+    "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant "
+    "first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning "
+    "process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., "
+    "<think> reasoning process here </think><answer> answer here </answer>"
+)
+data = []
+os.makedirs(f"{out_root}/{dataset_name}", exist_ok=True)
+for idx, d in tqdm(enumerate(ds), total=len(ds)):
+    base_image_path = f"{dataset_name}/{idx}.png"
+    image_path = f"{out_root}/{base_image_path}"
+    image = d['image']
+    image.save(image_path)
+    conversations = [
+        {
+            "role": "user",
+            "content": "<image>\n" + d['problem']
+        },
+        {
+            "role": "assistant",
+            "content": d['solution']
+        }
+    ]
+    data.append({
+        "conversations": conversations,
+        "images": [base_image_path],
+        "system": SYSTEM_PROMPT
+    })
+
+output_path = f"{out_root}/{dataset_name}.json"
+with open(output_path, "w") as f:
+    json.dump(data, f, indent=4, ensure_ascii=False)
+
+with open(f"{out_root}/dataset_info.json", "r") as f:
+    dataset_info = json.load(f)
+
+dataset_info[dataset_name] = {
+        "file_name": f"{dataset_name}.json",
+        "formatting": "sharegpt",
+        "columns": {
+            "messages": "conversations",
+            "images": "images",
+            "system": "system"
+        },
+        "tags": {
+            "role_tag": "role",
+            "content_tag": "content",
+            "user_tag": "user",
+            "assistant_tag": "assistant"
+        }
+}
+
+with open(f"{out_root}/dataset_info.json", "w") as f:
+    json.dump(dataset_info, f, indent=4, ensure_ascii=False)