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| """ | |
| Convert alpaca dataset into sharegpt format. | |
| Usage: python3 -m fastchat.data.convert_alpaca --in alpaca_data.json | |
| """ | |
| import argparse | |
| import json | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import numpy as np | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--in-file", type=str) | |
| parser.add_argument("--out-file", type=str) | |
| args = parser.parse_args() | |
| content = json.load(open(args.in_file, "r")) | |
| new_content = [] | |
| for i, c in enumerate(content): | |
| if len(c["input"].strip()) > 1: | |
| q, a = c["instruction"] + "\nInput:\n" + c["input"], c["output"] | |
| else: | |
| q, a = c["instruction"], c["output"] | |
| new_content.append( | |
| { | |
| "id": f"alpaca_{i}", | |
| "conversations": [ | |
| {"from": "human", "value": q}, | |
| {"from": "gpt", "value": a}, | |
| ], | |
| } | |
| ) | |
| print(f"#out: {len(new_content)}") | |
| json.dump(new_content, open(args.out_file, "w"), indent=2, ensure_ascii=False) | |