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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 | # 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]",
# "Pillow>=9.4.0",
# "torchvision",
# "trackio",
# "kernels",
# ]
# ///
"""
Without dataset streaming:
```
accelerate launch examples/scripts/dpo_vlm.py \
--dataset_name HuggingFaceH4/rlaif-v_formatted \
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 32 \
--dataset_num_proc 32 \
--output_dir dpo_qwen_2_5_rlaif-v \
--dtype bfloat16 \
--use_peft \
--lora_target_modules all-linear
```
With dataset streaming:
```
accelerate launch examples/scripts/dpo_vlm.py \
--dataset_name HuggingFaceH4/rlaif-v_formatted \
--dataset_streaming \
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
--per_device_train_batch_size 2 \
--max_steps 100 \
--gradient_accumulation_steps 32 \
--dataset_num_proc 32 \
--output_dir dpo_qwen_2_5_rlaif-v \
--dtype bfloat16 \
--use_peft \
--lora_target_modules all-linear
```
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForImageTextToText, AutoProcessor
from trl import (
DPOConfig,
DPOTrainer,
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, DPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
################
# Model & Processor
################
dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
dtype=dtype,
)
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
model = AutoModelForImageTextToText.from_pretrained(
model_args.model_name_or_path,
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
peft_config = get_peft_config(model_args)
processor = AutoProcessor.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, do_image_splitting=False
)
if script_args.ignore_bias_buffers:
# torch distributed hack
model._ddp_params_and_buffers_to_ignore = [
name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
]
################
# Dataset
################
dataset = load_dataset(
script_args.dataset_name,
name=script_args.dataset_config,
streaming=script_args.dataset_streaming,
)
################
# Training
################
trainer = DPOTrainer(
model,
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,
peft_config=peft_config,
)
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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