File size: 2,297 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 | # 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",
# "math-verify",
# "latex2sympy2_extended",
# "trackio",
# ]
# ///
"""
pip install math_verify
CUDA_VISIBLE_DEVICES=1 VLLM_SERVER_DEV_MODE=1 vllm serve Qwen/Qwen3-0.6B \
--max-model-len 2048 \
--logprobs-mode processed_logprobs \
--weight-transfer-config '{"backend":"nccl"}'
CUDA_VISIBLE_DEVICES=0 accelerate launch examples/scripts/async_grpo.py
"""
from datasets import load_dataset
from trl.experimental.async_grpo import AsyncGRPOConfig, AsyncGRPOTrainer
from trl.rewards import accuracy_reward
def format_sample(sample):
return {
"prompt": [{"role": "user", "content": sample["question"]}],
"solution": sample["answer"].split("####")[-1].strip(),
}
def main() -> None:
dataset = load_dataset("openai/gsm8k", "main", split="train")
dataset = dataset.map(format_sample, remove_columns=dataset.column_names)
config = AsyncGRPOConfig(
output_dir="async_grpo_gsm8k",
save_strategy="no",
per_device_train_batch_size=16,
gradient_accumulation_steps=2,
max_completion_length=1024,
chat_template_kwargs={"enable_thinking": False},
max_steps=200,
learning_rate=1e-5,
report_to="trackio",
trackio_space_id="async-grpo-gsm8k",
project="async-grpo-gsm8k",
log_completions=True,
)
trainer = AsyncGRPOTrainer(
model="Qwen/Qwen3-0.6B",
args=config,
train_dataset=dataset,
reward_funcs=accuracy_reward,
)
trainer.train()
if __name__ == "__main__":
main()
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