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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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.
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, Mxfp4Config
from trl import (
ModelConfig,
ScriptArguments,
SFTConfig,
SFTTrainer,
TrlParser,
get_peft_config,
)
def main(script_args, training_args, model_args):
# ------------------------
# Load model & tokenizer
# ------------------------
quantization_config = Mxfp4Config(dequantize=True)
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=model_args.torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
quantization_config=quantization_config,
)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, **model_kwargs
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
)
# --------------
# Load dataset
# --------------
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# -------------
# Train model
# -------------
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split]
if training_args.eval_strategy != "no"
else None,
# You had `processing_class` here, but SFTTrainer expects `tokenizer`
tokenizer=tokenizer,
peft_config=get_peft_config(model_args),
)
trainer.train()
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
# You had dataset_name here, but it's not a valid argument for push_to_hub in this context.
# It will use the hub_model_id from TrainingArguments.
trainer.push_to_hub()
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
script_args, training_args, model_args, _ = parser.parse_args_and_config(
return_remaining_strings=True
)
main(script_args, training_args, model_args)
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