implement uniflow demo with basic Flow for use with SFT

example/uniflow/uniflow_sft_demo.py

@@ -0,0 +1,44 @@
+"""Demo for using uniflow to generate data for supervised fine tuning.
+
+python -m example.uniflow.uniflow_sft_demo
+"""
+import os
+import pandas as pd
+
+from uniflow.flow.flow import Flow
+from pykoi.rlhf import RLHFConfig
+from pykoi.rlhf import SupervisedFinetuning
+from pykoi.chat.db.constants import (
+    QA_CSV_HEADER_ID,
+    QA_CSV_HEADER_QUESTION,
+    QA_CSV_HEADER_ANSWER,
+    QA_CSV_HEADER_VOTE_STATUS)
+
+CSV_FILENAME = "qd_immigration"
+CSV_OUTPUT_SUFFIX = "-flow-output"
+
+# Load data
+current_directory = os.getcwd()
+qaa = pd.read_csv(f"{current_directory}/{CSV_FILENAME}.csv", encoding="utf8")
+
+# run flow
+flow = Flow()
+output_dict = flow(qaa)
+
+# save new data to csv
+df = pd.DataFrame(output_dict["output"][0], columns=[
+    QA_CSV_HEADER_ID,
+    QA_CSV_HEADER_QUESTION,
+    QA_CSV_HEADER_ANSWER,
+    QA_CSV_HEADER_VOTE_STATUS])
+df.to_csv(f"{current_directory}/{CSV_FILENAME}{CSV_OUTPUT_SUFFIX}.csv", index=False)
+
+# analyze the data
+print("Flow save successful!")
+print(df)
+print(f"The output csv file {CSV_FILENAME}{CSV_OUTPUT_SUFFIX}.csv has {df.shape[0]} rows in total")
+
+# run supervised finetuning
+config = RLHFConfig(base_model_path="databricks/dolly-v2-3b", dataset_type="local_csv", dataset_name=f"{CSV_FILENAME}{CSV_OUTPUT_SUFFIX}.csv")
+rlhf_step1_sft = SupervisedFinetuning(config)
+rlhf_step1_sft.train_and_save("./models/rlhf_step1_sft")