mirror of
https://github.com/huggingface/trl.git
synced 2025-10-20 18:43:52 +08:00
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
# 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.
|
|
|
|
# /// script
|
|
# dependencies = [
|
|
# "trl[vllm]",
|
|
# "peft",
|
|
# "math-verify",
|
|
# "latex2sympy2_extended",
|
|
# "trackio",
|
|
# "kernels",
|
|
# ]
|
|
# ///
|
|
|
|
"""
|
|
pip install math_verify num2words==0.5.14 peft trackio vllm
|
|
export TRACKIO_PROJECT="RLOO-NuminaMath-TIR"
|
|
accelerate launch --config_file examples/accelerate_configs/deepspeed_zero3.yaml examples/scripts/rloo.py
|
|
"""
|
|
|
|
import os
|
|
|
|
import torch
|
|
from datasets import load_dataset
|
|
from peft import LoraConfig
|
|
|
|
from trl import RLOOConfig, RLOOTrainer
|
|
from trl.rewards import accuracy_reward, think_format_reward
|
|
|
|
|
|
# Enable logging in a Hugging Face Space
|
|
os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
|
|
|
|
|
|
def main():
|
|
# Dataset
|
|
train_dataset, eval_dataset = load_dataset("AI-MO/NuminaMath-TIR", split=["train[:5%]", "test[:5%]"])
|
|
|
|
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):
|
|
return {
|
|
"prompt": [
|
|
{"role": "system", "content": SYSTEM_PROMPT},
|
|
{"role": "user", "content": example["problem"]},
|
|
],
|
|
}
|
|
|
|
train_dataset = train_dataset.map(make_conversation, remove_columns=["messages", "problem"])
|
|
eval_dataset = eval_dataset.map(make_conversation, remove_columns=["messages", "problem"])
|
|
|
|
# Training
|
|
training_args = RLOOConfig(
|
|
output_dir="Qwen3-0.6B-RLOO",
|
|
model_init_kwargs={"dtype": torch.bfloat16},
|
|
learning_rate=1e-5,
|
|
gradient_checkpointing_kwargs=dict(use_reentrant=False),
|
|
log_completions=True,
|
|
num_completions_to_print=2,
|
|
max_prompt_length=2048,
|
|
max_completion_length=1024,
|
|
gradient_accumulation_steps=2,
|
|
steps_per_generation=8,
|
|
use_vllm=True,
|
|
vllm_mode="colocate",
|
|
vllm_gpu_memory_utilization=0.5,
|
|
run_name="Qwen3-0.6B-RLOO-NuminaMath-TIR",
|
|
)
|
|
|
|
trainer = RLOOTrainer(
|
|
model="Qwen/Qwen3-0.6B",
|
|
args=training_args,
|
|
reward_funcs=[think_format_reward, accuracy_reward],
|
|
train_dataset=train_dataset,
|
|
eval_dataset=eval_dataset,
|
|
peft_config=LoraConfig(),
|
|
)
|
|
|
|
trainer.train()
|
|
|
|
# Save and push to hub
|
|
trainer.save_model(training_args.output_dir)
|
|
trainer.push_to_hub(dataset_name="AI-MO/NuminaMath-TIR")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|