HF_ML_Tasksmith / tasks /tasksmith-0df9b3cd6191 /environment /source /examples /scripts /distillation.py
| # 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", | |
| # "peft", | |
| # "trackio", | |
| # "kernels", | |
| # ] | |
| # /// | |
| # docstyle-ignore | |
| """ | |
| # Full training (off-policy only, lmbda=0): | |
| ``` | |
| python examples/scripts/distillation.py \ | |
| --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ | |
| --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \ | |
| --dataset_name trl-lib/chatbot_arena_completions \ | |
| --learning_rate 2e-5 \ | |
| --per_device_train_batch_size 4 \ | |
| --gradient_accumulation_steps 8 \ | |
| --lmbda 0.0 \ | |
| --output_dir distilled-model \ | |
| --num_train_epochs 1 | |
| ``` | |
| # Mixed on/off-policy (lmbda=0.5): | |
| ``` | |
| python examples/scripts/distillation.py \ | |
| --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ | |
| --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \ | |
| --dataset_name trl-lib/chatbot_arena_completions \ | |
| --learning_rate 2e-5 \ | |
| --per_device_train_batch_size 4 \ | |
| --gradient_accumulation_steps 8 \ | |
| --lmbda 0.5 \ | |
| --beta 0.5 \ | |
| --output_dir distilled-model \ | |
| --num_train_epochs 1 | |
| ``` | |
| # LoRA: | |
| ``` | |
| python examples/scripts/distillation.py \ | |
| --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ | |
| --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \ | |
| --dataset_name trl-lib/chatbot_arena_completions \ | |
| --learning_rate 2e-4 \ | |
| --per_device_train_batch_size 4 \ | |
| --gradient_accumulation_steps 8 \ | |
| --lmbda 0.0 \ | |
| --output_dir distilled-model \ | |
| --num_train_epochs 1 \ | |
| --use_peft \ | |
| --lora_r 64 \ | |
| --lora_alpha 16 | |
| ``` | |
| """ | |
| import argparse | |
| import os | |
| # Enable logging in a Hugging Face Space | |
| os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio") | |
| def main(script_args, training_args, model_args): | |
| from datasets import load_dataset | |
| from transformers import GenerationConfig | |
| from trl import ( | |
| LogCompletionsCallback, | |
| get_kbit_device_map, | |
| get_peft_config, | |
| get_quantization_config, | |
| ) | |
| from trl.experimental.distillation import DistillationTrainer | |
| ################ | |
| # Model init kwargs | |
| ################ | |
| quantization_config = get_quantization_config(model_args) | |
| model_kwargs = dict( | |
| revision=model_args.model_revision, | |
| attn_implementation=model_args.attn_implementation, | |
| dtype=model_args.dtype, | |
| use_cache=False if training_args.gradient_checkpointing else True, | |
| device_map=get_kbit_device_map() if quantization_config is not None else None, | |
| quantization_config=quantization_config, | |
| ) | |
| training_args.model_init_kwargs = model_kwargs | |
| teacher_model_kwargs = dict( | |
| revision=training_args.teacher_model_revision, | |
| attn_implementation=model_args.attn_implementation, | |
| dtype=model_args.dtype, | |
| use_cache=True, | |
| device_map=get_kbit_device_map() if quantization_config is not None else None, | |
| quantization_config=quantization_config, | |
| ) | |
| if training_args.teacher_model_init_kwargs is not None: | |
| teacher_model_kwargs.update(training_args.teacher_model_init_kwargs) | |
| training_args.teacher_model_init_kwargs = teacher_model_kwargs | |
| ################ | |
| # Dataset | |
| ################ | |
| dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config) | |
| ################ | |
| # Training | |
| ################ | |
| eval_dataset = None | |
| if training_args.eval_strategy != "no": | |
| if script_args.dataset_test_split in dataset: | |
| eval_dataset = dataset[script_args.dataset_test_split] | |
| elif "validation" in dataset: | |
| eval_dataset = dataset["validation"] | |
| elif "dev" in dataset: | |
| eval_dataset = dataset["dev"] | |
| trainer = DistillationTrainer( | |
| model=model_args.model_name_or_path, | |
| teacher_model=training_args.teacher_model_name_or_path, | |
| args=training_args, | |
| train_dataset=dataset[script_args.dataset_train_split], | |
| eval_dataset=eval_dataset, | |
| peft_config=get_peft_config(model_args), | |
| ) | |
| if training_args.eval_strategy != "no": | |
| generation_config = GenerationConfig( | |
| max_new_tokens=training_args.max_completion_length, do_sample=True, temperature=training_args.temperature | |
| ) | |
| completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8) | |
| trainer.add_callback(completions_callback) | |
| trainer.train() | |
| # Save and push to Hub | |
| trainer.save_model(training_args.output_dir) | |
| if training_args.push_to_hub: | |
| trainer.push_to_hub(dataset_name=script_args.dataset_name) | |
| def make_parser(subparsers: argparse._SubParsersAction | None = None, prog: str | None = None): | |
| from trl import ModelConfig, ScriptArguments, TrlParser | |
| from trl.experimental.distillation import DistillationConfig | |
| dataclass_types = (ScriptArguments, DistillationConfig, ModelConfig) | |
| if subparsers is not None: | |
| parser = subparsers.add_parser( | |
| "distillation", help="Run the distillation training script", dataclass_types=dataclass_types | |
| ) | |
| else: | |
| parser = TrlParser(dataclass_types, prog=prog) | |
| return parser | |
| if __name__ == "__main__": | |
| parser = make_parser() | |
| script_args, training_args, model_args = parser.parse_args_and_config(fail_with_unknown_args=False) | |
| main(script_args, training_args, model_args) | |