# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from llamafactory.train.tuner import export_model, run_exp DEMO_DATA = os.environ.get("DEMO_DATA", "llamafactory/demo_data") TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3") TINY_LLAMA_ADAPTER = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora") TRAIN_ARGS = { "model_name_or_path": TINY_LLAMA, "do_train": True, "finetuning_type": "lora", "dataset_dir": "REMOTE:" + DEMO_DATA, "template": "llama3", "cutoff_len": 1, "overwrite_cache": False, "overwrite_output_dir": True, "per_device_train_batch_size": 1, "max_steps": 1, } INFER_ARGS = { "model_name_or_path": TINY_LLAMA, "adapter_name_or_path": TINY_LLAMA_ADAPTER, "finetuning_type": "lora", "template": "llama3", "infer_dtype": "float16", "export_dir": "llama3_export", } OS_NAME = os.environ.get("OS_NAME", "") @pytest.mark.parametrize( "stage,dataset", [ ("pt", "c4_demo"), ("sft", "alpaca_en_demo"), ("dpo", "dpo_en_demo"), ("kto", "kto_en_demo"), pytest.param("rm", "dpo_en_demo", marks=pytest.mark.xfail(OS_NAME.startswith("windows"), reason="OS error.")), ], ) def test_run_exp(stage: str, dataset: str): output_dir = "train_{}".format(stage) run_exp({"stage": stage, "dataset": dataset, "output_dir": output_dir, **TRAIN_ARGS}) assert os.path.exists(output_dir) def test_export(): export_model(INFER_ARGS) assert os.path.exists("llama3_export")