mirror of
https://github.com/huggingface/trl.git
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100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# /// script
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# dependencies = [
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# "trl",
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# "kernels",
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# "trackio",
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# "kernels",
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# ]
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# ///
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"""
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pip install –-upgrade kernels
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Example:
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accelerate launch \
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--config_file examples/accelerate_configs/deepspeed_zero3.yaml \
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examples/scripts/sft_gpt_oss.py \
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--dtype bfloat16 \
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--model_name_or_path openai/gpt-oss-20b \
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--packing \
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--run_name 20b-full-eager \
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--attn_implementation kernels-community/vllm-flash-attn3 \
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--dataset_num_proc 12 \
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--dataset_name HuggingFaceH4/Multilingual-Thinking \
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--gradient_checkpointing \
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--max_length 4096 \
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--per_device_train_batch_size 2 \
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--num_train_epochs 1 \
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--logging_steps 1 \
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--warmup_ratio 0.03 \
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--lr_scheduler_type cosine_with_min_lr \
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--lr_scheduler_kwargs '{"min_lr_rate": 0.1}' \
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--output_dir gpt-oss-20b-multilingual-reasoner \
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--report_to trackio \
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--seed 42
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"""
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import os
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM, Mxfp4Config
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from trl import ModelConfig, ScriptArguments, SFTConfig, SFTTrainer, TrlParser, get_peft_config
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# Enable logging in a Hugging Face Space
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os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
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def main(script_args, training_args, model_args):
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# Load model
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quantization_config = Mxfp4Config(dequantize=True)
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model_kwargs = dict(
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revision=model_args.model_revision,
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trust_remote_code=model_args.trust_remote_code,
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attn_implementation=model_args.attn_implementation,
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dtype=model_args.dtype,
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use_cache=False if training_args.gradient_checkpointing else True,
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quantization_config=quantization_config,
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)
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model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
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# Load dataset
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dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
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# Train model
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=dataset[script_args.dataset_train_split],
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eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
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peft_config=get_peft_config(model_args),
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)
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trainer.train()
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trainer.save_model(training_args.output_dir)
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if training_args.push_to_hub:
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trainer.push_to_hub(dataset_name=script_args.dataset_name)
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if __name__ == "__main__":
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parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
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script_args, training_args, model_args, _ = parser.parse_args_and_config(return_remaining_strings=True)
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main(script_args, training_args, model_args)
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