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62e8dff | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | # 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)
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