File size: 5,227 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # 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",
# "math-verify",
# "latex2sympy2_extended",
# "torchvision",
# "trackio",
# "kernels",
# ]
# ///
"""
pip install math_verify
# For Qwen/Qwen2.5-VL-3B-Instruct
accelerate launch \
--config_file examples/accelerate_configs/deepspeed_zero3.yaml \
examples/scripts/rloo_vlm.py \
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
--output_dir rloo-Qwen2.5-VL-3B-Instruct \
--learning_rate 1e-5 \
--dtype bfloat16 \
--max_completion_length 1024 \
--use_vllm \
--vllm_mode colocate \
--use_peft \
--lora_target_modules "q_proj", "v_proj" \
--log_completions
# For HuggingFaceTB/SmolVLM2-2.2B-Instruct
pip install num2words==0.5.14
accelerate launch \
--config_file examples/accelerate_configs/deepspeed_zero3.yaml \
examples/scripts/rloo_vlm.py \
--model_name_or_path HuggingFaceTB/SmolVLM2-2.2B-Instruct \
--output_dir rloo-SmolVLM2-2.2B-Instruct \
--learning_rate 1e-5 \
--dtype bfloat16 \
--max_completion_length 1024 \
--use_peft \
--lora_target_modules "q_proj", "v_proj" \
--log_completions \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 2 \
--num_generations 2
"""
import torch
from datasets import load_dataset
from trl import (
ModelConfig,
RLOOConfig,
RLOOTrainer,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.rewards import accuracy_reward, think_format_reward
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, RLOOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
################
# Model
################
dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
training_args.model_init_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.
training_args.model_init_kwargs["device_map"] = get_kbit_device_map()
training_args.model_init_kwargs["quantization_config"] = quantization_config
################
# Dataset
################
dataset = load_dataset("lmms-lab/multimodal-open-r1-8k-verified", split="train")
dataset = dataset.train_test_split(test_size=100, seed=42)
SYSTEM_PROMPT = (
"A conversation between user and assistant. The user asks a question, and the assistant solves it. The "
"assistant first thinks about the reasoning process in the mind and then provides the user with the answer. "
"The reasoning process and answer are enclosed within <think></think> tags, i.e., <think>\nThis is my "
"reasoning.\n</think>\nThis is my answer."
)
def make_conversation(example):
prompt = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": example["problem"]},
]
return {"prompt": prompt}
dataset = dataset.map(make_conversation)
# Filter have big images
def filter_big_images(example):
image = example["image"]
return image.size[0] < 512 and image.size[1] < 512
dataset = dataset.filter(filter_big_images)
def convert_to_rgb(example):
image = example["image"]
if image.mode != "RGB":
image = image.convert("RGB")
example["image"] = image
return example
dataset = dataset.map(convert_to_rgb)
train_dataset = dataset["train"]
eval_dataset = dataset["test"] if training_args.eval_strategy != "no" else None
################
# Training
################
trainer = RLOOTrainer(
model=model_args.model_name_or_path,
args=training_args,
reward_funcs=[think_format_reward, accuracy_reward],
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=get_peft_config(model_args),
)
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)
|