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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.
"""
Train Gemma-3 on the Codeforces COTS dataset.
accelerate launch --config_file examples/accelerate_configs/deepspeed_zero3.yaml examples/scripts/sft_gemma3.py
"""
from datasets import load_dataset
from transformers import AutoModelForImageTextToText
from trl import SFTConfig, SFTTrainer
def main():
# Load dataset
train_dataset = load_dataset("open-r1/codeforces-cots", split="train")
train_dataset = train_dataset.remove_columns("prompt")
# Load model
model_id = "google/gemma-3-12b-it"
model = AutoModelForImageTextToText.from_pretrained(model_id, attn_implementation="eager")
# Train model
training_args = SFTConfig(
output_dir=f"{model_id}-codeforces-SFT",
logging_steps=10,
bf16=True,
use_liger_kernel=True,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
max_length=8192,
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
dataset_num_proc=32,
num_train_epochs=1,
)
trainer = SFTTrainer(
args=training_args,
model=model,
train_dataset=train_dataset,
)
trainer.train()
# Push to hub
trainer.push_to_hub(dataset_name="open-r1/codeforces-cots")
if __name__ == "__main__":
main()
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