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"""
Description: This script is used to post-process the data after getting it from the LLM.
input: data from llm
output: data for llama finetuning
"""
import json
import os
import copy
def save_data(data, dataset_name, model_name):
file_dir = f"../data/finetune/{dataset_name}"
os.makedirs(file_dir, exist_ok=True)
file_path = os.path.join(file_dir, f"llama_ft_{model_name}.jsonl")
with open(file_path, "w") as f:
for d in data:
f.write(json.dumps(d) + "\n")
print(f"Data saved to {file_path}")
def process(sample, dataset_name, model_name):
"""
input: sample from llm
output: sample for llama finetuning
"""
output_format = {"conversations": [{"role": "user", "content": ""}, {"role": "assistant", "content": ""}]}
ft_list = []
for i in range(len(sample)):
output = copy.deepcopy(output_format)
output["conversations"][0]["content"] = sample[i]["query"]
output["conversations"][1]["content"] = str(sample[i]["answer"])
ft_list.append(output)
# save data
save_data(ft_list, dataset_name, model_name)
# ***** Main *****
if __name__ == "__main__":
# read data
dataset_name_list = ["mag"]
model_names = ["gpt-4o-mini-20240718", "o3-mini-2025-01-31", "gpt-o1-2024-12-17", "gpt-4o-2024-05-13", "gpt-4o-mini-20240718", "gpt35-1106"] # gpt-o1-2024-12-17, "gpt-4o-mini-20240718", "gpt35-1106", o3-mini-2025-01-31
for dataset_name in dataset_name_list:
for model_name in model_names:
relative_path = f"finetune/{dataset_name}/1000.json" # f"finetune/{dataset_name}/1000_{dataset_name}.json"
current_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Directory of the current script
file_path = os.path.join(current_dir, relative_path)
with open(f"{file_path}", "r") as f:
sample = json.load(f)
# process data
process(sample, dataset_name, model_name)
print(f"Processing {model_name} for {dataset_name} is done.")
break |