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1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # Copyright 2020-2026 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.
# /// script
# dependencies = [
# "trl",
# "kernels",
# "trackio",
# "kernels",
# ]
# ///
"""
pip install –-upgrade kernels
Example:
accelerate launch \
--config_file examples/accelerate_configs/deepspeed_zero3.yaml \
examples/scripts/sft_gpt_oss.py \
--dtype bfloat16 \
--model_name_or_path openai/gpt-oss-20b \
--packing \
--run_name 20b-full-eager \
--attn_implementation kernels-community/vllm-flash-attn3 \
--dataset_num_proc 12 \
--dataset_name HuggingFaceH4/Multilingual-Thinking \
--max_length 4096 \
--per_device_train_batch_size 2 \
--num_train_epochs 1 \
--logging_steps 1 \
--warmup_steps 0.03 \
--lr_scheduler_type cosine_with_min_lr \
--lr_scheduler_kwargs '{"min_lr_rate": 0.1}' \
--output_dir gpt-oss-20b-multilingual-reasoner \
--report_to trackio \
--seed 42
"""
from datasets import load_dataset
from transformers import AutoModelForCausalLM, Mxfp4Config
from trl import ModelConfig, ScriptArguments, SFTConfig, SFTTrainer, TrlParser, get_peft_config
def main(script_args, training_args, model_args):
# Load model
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,
dtype=model_args.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)
# 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,
peft_config=get_peft_config(model_args),
)
trainer.train()
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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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