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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)
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