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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 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 | # 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",
# ]
# ///
"""
# 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 \
--num_train_epochs 1 \
--push_to_hub
# 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 \
--num_train_epochs 1 \
--push_to_hub \
--use_peft \
--lora_r 64 \
--lora_alpha 16
"""
from datasets import load_dataset
from transformers import AutoTokenizer, GenerationConfig
from trl import (
LogCompletionsCallback,
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.experimental.gkd import GKDConfig, GKDTrainer
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, GKDConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
################
# Model & Tokenizer
################
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 = get_quantization_config(model_args)
if quantization_config is not None:
# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["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,
dtype=model_args.dtype,
use_cache=True,
)
if quantization_config is not None:
# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["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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