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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.
"""
# Full training:
python examples/scripts/gkd.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--teacher_model_name_or_path Qwen/Qwen2-1.5B-Instruct \
--dataset_name trl-lib/chatbot_arena_completions \
--learning_rate 2e-5 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 8 \
--output_dir gkd-model \
--logging_steps 10 \
--num_train_epochs 1 \
--push_to_hub \
--gradient_checkpointing
# LoRA:
python examples/scripts/gkd.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--teacher_model_name_or_path Qwen/Qwen2-1.5B-Instruct \
--dataset_name trl-lib/chatbot_arena_completions \
--learning_rate 2e-4 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 8 \
--output_dir gkd-model \
--logging_steps 10 \
--num_train_epochs 1 \
--push_to_hub \
--gradient_checkpointing \
--use_peft \
--lora_r 64 \
--lora_alpha 16
"""
from datasets import load_dataset
from transformers import AutoTokenizer, GenerationConfig
from trl import (
GKDConfig,
GKDTrainer,
LogCompletionsCallback,
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, GKDConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
################
# Model & Tokenizer
################
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=model_args.torch_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=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=model_args.torch_dtype,
use_cache=True,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
training_args.teacher_model_init_kwargs = teacher_model_kwargs
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
padding_side="left",
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
################
# Training
################
trainer = GKDTrainer(
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=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
)
if training_args.eval_strategy != "no":
generation_config = GenerationConfig(
max_new_tokens=training_args.max_new_tokens, 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)
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