機器學習 | 從0開發大模型之復現DeepSeek的aha moment

作者：周末程序猿 2025-04-03 15:46:53

最近申請了48G的顯存，結合一些開源的方案復現aha monent，并給出完整的代碼和工具鏈。

前面一篇文章介紹了《從0開發大模型之DeepSeek的GRPO》，并且實現了一個簡單版本的 GRPO 代碼，不過從工程領域來看，并沒有復現DeepSeek-R1，于是最近申請了48G的顯存，結合一些開源的方案復現aha monent，并給出完整的代碼和工具鏈。

1、什么是 `aha monent`

DeepSeek-R1 論文中提到，模型讓作者「見證了強化學習的力量和美感」，在DeepSeek-R1-Zero的中間版本，「頓悟時刻」來了：模型學會了以人類的語氣進行反思。

aha monent

2、使用什么的基座模型和訓練數據

由于顯卡只有48G，可以用基座模型Qwen2.5，模型大小：0.5B，1.5B，3B
訓練數據有很多：（可以直接在huggingface上找到）

a.AI-MO/NuminaMath-TIR：包括72K行的數學問題，解決方案和答案，是從 NuminaMath-CoT 數據集提煉出來的

b. FreedomIntelligence/medical-o1-verifiable-problem：包括40K行的醫學數據，不過沒有相關的推理過程

c.https://raw.githubusercontent.com/hkust-nlp/simpleRL-reason/refs/heads/main/train/data/math_level3to5_data_processed_with_qwen_prompt.json：在simpleRL-reason的開源項目中的訓練數據集

3、如何訓練

3.1、設計獎勵函數

從上一篇《從0開發大模型之DeepSeek的GRPO》中已經了解GRPO的原理，其中一部分是包括獎勵函數的設計，其中如何設計這里就省略，本文暫時參考其他復現R1的項目設使用了5個函數：

accuracy_reward：驗證答案的準確性，對就返回1，不對就返回0
format_reward：驗證格式的準確性，如果符合^<think>.*?</think><answer>.*?</answer>$的返回則返回1，否則就返回0
reasoning_steps_reward：有推理步驟的，類似(Step \d+:|^\d+\.|\n-|\n\*|First,|Second,|Next,|Finally,)，最大值返回3，否則返回0
cosine_reward：基于答案的長度做余弦，分為正確答案最大長度，正確答案最小長度，錯誤答案最大長度，錯誤答案最小長度
repetition_penalty_reward：計算 N-gram 重復獎勵
length_reward：參考kimi1.5的論文（https://arxiv.org/abs/2501.12599）

a.正確答案長度獎勵: reward = 0.5 - (len - min_len)/(max_len - min_len)

b.錯誤答案長度獎勵: reward = min(0, 0.5 - (len - min_len)/(max_len - min_len))

3.2、使用vLLM

為了提升性能和節省顯存，這里使用了vLLM，vLLM是一個開源的大模型推理加速框架，通過PagedAttention高效地管理attention中緩存的張量，實現比HuggingFace Transformers高14-24倍的吞吐量，從本文實驗過程中發現，之前需要60G顯存的，基本40G就能跑起來。

由于vLLM的加載模型和Huggingface的可以直接兼容，所以可以參考如下代碼跑起來：

from vllm import LLM, SamplingParams
if __name__ == '__main__':
    model_path = "{模型名稱}"
    model = LLM(model=model_path, 
        tensor_parallel_size=1, 
        trust_remote_code=True, 
        max_model_len=10000, 
        enforce_eager=True, 
        gpu_memory_utilizatinotallow=0.5, 
        block_size=32)
    sampling_params = SamplingParams(temperature=0, max_tokens=1, prompt_logprobs=20)

    prompt = "vLLM是如何實現的?"
    response = model.generate(prompt, sampling_params, use_tqdm=False)[0]
    print(response, '\n\n', response.outputs)

3.3、使用`Accelerate`和`deepspeed`加速訓練

Accelerate是PyTorch官方提供的分布式訓練工具，而deepspeed是由Microsoft提供的分布式訓練工具，最主要的區別在于支持的模型規模不同，deepspeed支持更大規模的模型，deepspeed還提供了更多的優化策略和工具，例如ZeRO和Offload等，Accelerate更加穩定和易于使用，適合中小規模的訓練任務，不過huggingface已經集成了deepspeed，如果對于訓練改幾行代碼即可，如下：

#!pip install accelerate
#!pip install deepspeed
import torch
import torch.nn.functional as F
from datasets import load_dataset
# 引入基礎庫accelerate
from accelerate import Accelerator

# 創建accelerator
accelerator = Accelerator()
# 修改設備信息
device = accelerator.device
model = torch.nn.Transformer().to(device)
optimizer = torch.optim.Adam(model.parameters())

dataset = load_dataset({需要加載的數據})
data = torch.utils.data.DataLoader(dataset, shuffle=True)

# 使用accelerator訓練
model, optimizer, data = accelerator.prepare(model, optimizer, data)
model.train()
for epoch in range(10):
    for source, targets in data:
        source = source.to(device)
        targets = targets.to(device)

        optimizer.zero_grad()

        output = model(source)
        loss = F.cross_entropy(output, targets)

        # 使用accelerator做backward
        accelerator.backward(loss)

        optimizer.step()

相關的配置可以參考zero3.yaml文件或者運行accelerate config。

4、完整的代碼

4.1、命令

需要安裝 python>=3.10 和必要的庫如下：

pip install transformers
pip install trl
pip install --upgrade trl
pip install latex2sympy2_extended math_verify
pip install flash_attn
pip install vllm
pip install deepspeed
pip install accelerate

運行的命令：

accelerate launch --config_file zero3.yaml 0-grpotrainer_r1.py

其中zero3.yaml配置：

compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
  deepspeed_multinode_launcher: standard
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero_stage: 3 
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 1 
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

4.2、代碼

完整的訓練代碼較大，請到本文的最后查看。

5、觀察`aha moment`

從上圖可以看出，模型從直接思考沒有解出問題，但是后面反復添加一些思考步驟就正確了。

6、注意事項

（1）安裝過程中錯誤：ImportError: FlashAttention2 has been toggled on, but it cannot be used due to the following error: the package flash_attn seems to be not installed. Please refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2.解決方案：
pip install -U flash-attn

（2）安裝過程中錯誤：ImportError: vLLM is not available and use_vllm is set to True. Please install vLLM with pip install vllm to use it.解決方案：
pip install -U vllm

（3）訓練完的模型如何轉換為運行的模型？解決方案：

from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict 

convert_zero_checkpoint_to_fp32_state_dict(
    checkpoint_dir="./output/GRPO-R1-1.5B",
    output_dir="./output/GRPO-R1-1.5B",
    tag="global_step9055", # 模型保存的step文件
)

（4）如果進行模型測試？解決方案：

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig

# 加載Qwen模型
# model_name = "Qwen/Qwen2.5-1.5B"
# 加載本地模型
model_name = "./output/GRPO-R1-1.5B"
model = AutoModelForCausalLM.from_pretrained(model_name, cache_dir="./model")

tokenizer = AutoTokenizer.from_pretrained(model_name)
model.eval()
device = torch.device("cuda"if torch.cuda.is_available() else"cpu")
print("device: ", device)
model.to(device)

chat_history_ids = None
whileTrue:
    user_input = input("用戶: ")
    if user_input.lower() == "exit":
        break

    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt').to(device)

    if chat_history_ids isnotNone:
        input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
    else:
        input_ids = new_user_input_ids

    chat_history_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
    bot_response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

    print("機器人: ", bot_response)

7、代碼

from typing import Optional, Dict
import re, logging, os, sys, torch, math
import transformers
from transformers import (
    AutoModelForCausalLM,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
import datasets
from datasets import load_dataset
from trl import ModelConfig, ScriptArguments, GRPOConfig, GRPOTrainer, get_peft_config
from dataclasses import dataclass, field
from latex2sympy2_extended import NormalizationConfig
from math_verify import LatexExtractionConfig, parse, verify

logger = logging.getLogger(__name__)

def verify_answer(contents, solution):
    rewards = []
    for content, sol in zip(contents, solution):
        gold_parsed = parse(
            sol,
            extraction_mode="first_match",
            extraction_cnotallow=[LatexExtractionConfig()],
        )
        print('-'*100)
        print(f'\ncontent:{content}\nsol:{sol}')
        if len(gold_parsed) != 0:
            answer_parsed = parse(
                content,
                extraction_cnotallow=[
                    LatexExtractionConfig(
                        normalization_cnotallow=NormalizationConfig(
                            nits=False,
                            malformed_operators=False,
                            basic_latex=True,
                            equatinotallow=True,
                            boxed="all",
                            units=True,
                        ),
                        # Ensures that boxed is tried first
                        boxed_match_priority=0,
                        try_extract_without_anchor=False,
                    )
                ],
                extraction_mode="first_match",
            )
            # Reward 1 if the content is the same as the ground truth, 0 otherwise
            reward = float(verify(answer_parsed, gold_parsed))
            print('-'*100)
            print(f'\nanswer_parsed:{answer_parsed}\ngold_parsed:{gold_parsed}\nreward:{reward}')
        else:
            reward = 1.0
            print(f'Failed to parse gold solution: {sol}')
        rewards.append(reward)

    return rewards

def accuracy_reward(completions, solution, **kwargs):
    """Reward function that checks if the completion is the same as the ground truth."""
    contents = [completion[0]["content"] for completion in completions]
    rewards = verify_answer(contents, solution)
    print(f'\naccuracy rewards:{rewards}')
    return rewards

def format_reward(completions, **kwargs):
    """Reward function that checks if the completion has a specific format."""
    pattern = r"^<think>.*?</think><answer>.*?</answer>$"
    completion_contents = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, content) for content in completion_contents]
    rewards = [1.0if match else0.0for match in matches]
    print('-'*100)
    print('\nformat rewards:', rewards)
    return rewards

def reasoning_steps_reward(completions, **kwargs):
    """Reward function that checks for clear step-by-step reasoning.
    Regex pattern:
        Step \d+: - matches "Step 1:", "Step 2:", etc.
        ^\d+\. - matches numbered lists like "1.", "2.", etc. at start of line
        \n- - matches bullet points with hyphens
        \n\* - matches bullet points with asterisks
        First,|Second,|Next,|Finally, - matches transition words
    """
    pattern = r"(Step \d+:|^\d+\.|\n-|\n\*|First,|Second,|Next,|Finally,)"
    completion_contents = [completion[0]["content"] for completion in completions]
    matches = [len(re.findall(pattern, content)) for content in completion_contents]
    # Magic nubmer 3 to encourage 3 steps and more, otherwise partial reward
    return [min(1.0, count / 3) for count in matches]

def len_reward(completions: list[Dict[str, str]], solution: list[str], **kwargs) -> float:
    """Compute length-based rewards to discourage overthinking and promote token efficiency.

    Taken from from the Kimi 1.5 tech report: https://arxiv.org/abs/2501.12599

    Args:
        completions: List of model completions
        solutions: List of ground truth solutions

    Returns:
        List of rewards where:
        - For correct answers: reward = 0.5 - (len - min_len)/(max_len - min_len)
        - For incorrect answers: reward = min(0, 0.5 - (len - min_len)/(max_len - min_len))
    """
    contents = [completion[0]["content"] for completion in completions]

    # First check correctness of answers
    correctness = verify_answer(contents, solution)

    # Calculate lengths
    lengths = [len(content) for content in contents]
    min_len = min(lengths)
    max_len = max(lengths)

    # If all responses have the same length, return zero rewards
    if max_len == min_len:
        return [0.0] * len(completions)

    rewards = []
    for length, is_correct in zip(lengths, correctness):
        lambda_val = 0.5 - (length - min_len) / (max_len - min_len)
        reward = lambda_val if is_correct > 0.0else min(0, lambda_val) 
        rewards.append(float(reward))

    return rewards

def get_cosine_scaled_reward(
    min_value_wrong: float = -1.0,
    max_value_wrong: float = -0.5,
    min_value_correct: float = 0.5,
    max_value_correct: float = 1.0,
    max_len: int = 1000,
):
    def cosine_scaled_reward(completions, solution, **kwargs):
        """Reward function that scales based on completion length using a cosine schedule.

        Shorter correct solutions are rewarded more than longer ones.
        Longer incorrect solutions are penalized less than shorter ones.

        Args:
            completions: List of model completions
            solution: List of ground truth solutions

        This function is parameterized by the following arguments:
            min_value_wrong: Minimum reward for wrong answers
            max_value_wrong: Maximum reward for wrong answers
            min_value_correct: Minimum reward for correct answers
            max_value_correct: Maximum reward for correct answers
            max_len: Maximum length for scaling
        """
        contents = [completion[0]["content"] for completion in completions]
        rewards = []
        correctness = verify_answer(contents, solution)
        lengths = [len(content) for content in contents]
        for gen_len, is_correct in zip(lengths, correctness):
            # Apply cosine scaling based on length
            progress = gen_len / max_len
            cosine = math.cos(progress * math.pi)

            if is_correct > 0:
                min_value = min_value_correct
                max_value = max_value_correct
            else:
                # Swap min/max for incorrect answers
                min_value = max_value_wrong
                max_value = min_value_wrong

            reward = min_value + 0.5 * (max_value - min_value) * (1.0 + cosine)
            rewards.append(float(reward))

        return rewards

    return cosine_scaled_reward


def get_repetition_penalty_reward(ngram_size: int, max_penalty: float):
    """
    Computes N-gram repetition penalty as described in Appendix C.2 of https://arxiv.org/abs/2502.03373.
    Reference implementation from: https://github.com/eddycmu/demystify-long-cot/blob/release/openrlhf/openrlhf/reward/repetition.py

    Args:
    ngram_size: size of the n-grams
    max_penalty: Maximum (negative) penalty for wrong answers
    """
    if max_penalty > 0:
        raise ValueError(f"max_penalty {max_penalty} should not be positive")

    def zipngram(text: str, ngram_size: int):
        words = text.lower().split()
        return zip(*[words[i:] for i in range(ngram_size)])

    def repetition_penalty_reward(completions, **kwargs) -> float:
        """
        reward function the penalizes repetitions
        ref implementation: https://github.com/eddycmu/demystify-long-cot/blob/release/openrlhf/openrlhf/reward/repetition.py

        Args:
            completions: List of model completions
        """

        contents = [completion[0]["content"] for completion in completions]
        rewards = []
        for completion in contents:
            if completion == "":
                rewards.append(0.0)
                continue
            if len(completion.split()) < ngram_size:
                rewards.append(0.0)
                continue

            ngrams = set()
            total = 0
            for ng in zipngram(completion, ngram_size):
                ngrams.add(ng)
                total += 1

            scaling = 1 - len(ngrams) / total
            reward = scaling * max_penalty
            rewards.append(reward)
        return rewards

    return repetition_penalty_reward

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> and <answer> </answer> tags, respectively, i.e., "
    "<think> reasoning process here </think><answer> answer here </answer>"
)

@dataclass
class R1GRPOScriptArguments(ScriptArguments):
    reward_funcs: list[str] = field(
        default_factory = lambda: ["accuracy", "format"],
        metadata = {
            "help": f"List of reward functions. Available options: 'accuracy', 'format', 'reasoning_steps', 'len', 'get_cosine_scaled', 'get_repetition_penalty'"
        },
    )
    cosine_min_value_wrong: float = field(
        default=0.0,
        metadata={"help": "Minimum reward for wrong answers"},
    )
    cosine_max_value_wrong: float = field(
        default=-0.5,
        metadata={"help": "Maximum reward for wrong answers"},
    )
    cosine_min_value_correct: float = field(
        default=0.5,
        metadata={"help": "Minimum reward for correct answers"},
    )
    cosine_max_value_correct: float = field(
        default=1.0,
        metadata={"help": "Maximum reward for correct answers"},
    )
    cosine_max_len: int = field(
        default=1000,
        metadata={"help": "Maximum length for scaling"},
    )
    repetition_n_grams: int = field(
        default=3,
        metadata={"help": "Number of n-grams for repetition penalty reward"},
    )
    repetition_max_penalty: float = field(
        default=-1.0,
        metadata={"help": "Maximum (negative) penalty for for repetition penalty reward"},
    )

@dataclass
class R1GRPOConfig(GRPOConfig):
    """
    args for callbacks, benchmarks etc
    """
    benchmarks: list[str] = field(
        default_factory=lambda: [], metadata={"help": "The benchmarks to run after training."}
    )
    callbacks: list[str] = field(
        default_factory=lambda: [], metadata={"help": "The callbacks to run during training."}
    )
    system_prompt: Optional[str] = field(
        default=None, metadata={"help": "The optional system prompt to use for benchmarking."}
    )


def main(script_args, training_args, model_args):
    # Set seed for reproducibility
    set_seed(training_args.seed)

    ###############
    # Setup logging
    ###############
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process a small summary
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Model parameters {model_args}")
    logger.info(f"Script parameters {script_args}")
    logger.info(f"Data parameters {training_args}")

    # Check for last checkpoint
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir):
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        logger.info(f"Last checkpoint detected, resuming training at {last_checkpoint=}.")
    if last_checkpoint isnotNoneand training_args.resume_from_checkpoint isNone:
        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")

    # Load the dataset
    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)

    # Get reward functions
    REWARD_FUNCS_REGISTRY = {
        "accuracy": accuracy_reward,
        "format": format_reward,
        "reasoning_steps": reasoning_steps_reward,
        "cosine": get_cosine_scaled_reward(
            min_value_wrnotallow=script_args.cosine_min_value_wrong,
            max_value_wrnotallow=script_args.cosine_max_value_wrong,
            min_value_correct=script_args.cosine_min_value_correct,
            max_value_correct=script_args.cosine_max_value_correct,
            max_len=script_args.cosine_max_len,
        ),
        "repetition_penalty": get_repetition_penalty_reward(
            ngram_size=script_args.repetition_n_grams,
            max_penalty=script_args.repetition_max_penalty,
        ),
        "length": len_reward,
    }
    reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]

    # Format into conversation
    def make_conversation(example):
        return {
            "prompt": [
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": example["problem"]},
            ],
        }

    dataset = dataset.map(make_conversation)
    for split in dataset:
        if"messages"in dataset[split].column_names:
            dataset[split] = dataset[split].remove_columns("messages")

    logger.info("*** Initializing model kwargs ***")
    torch_dtype = (
        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
    )

    training_args.gradient_checkpointing = True
    model_kwargs = dict(
        revision = model_args.model_revision,
        trust_remote_code = model_args.trust_remote_code,
        attn_implementation = model_args.attn_implementation,
        torch_dtype = torch_dtype,
        use_cache = Falseif training_args.gradient_checkpointing elseTrue,
    )

    model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, 
                                                 load_in_4bit=False, **model_kwargs)

    print(model_args.model_name_or_path)
    #############################
    # Initialize the R1GRPO trainer
    #############################
    trainer = GRPOTrainer(
        model = model,
        reward_funcs = reward_funcs,
        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"elseNone,
        peft_config = get_peft_config(model_args),
    )

    ###############
    # Training loop
    ###############
    logger.info("*** Train ***")
    checkpoint = None
    if training_args.resume_from_checkpoint isnotNone:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint isnotNone:
        checkpoint = last_checkpoint
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
    metrics = train_result.metrics
    metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

    ##################################
    # Save model and create model card
    ##################################
    logger.info("*** Save model ***")
    trainer.save_model(training_args.output_dir)
    logger.info(f"Model saved to {training_args.output_dir}")

    # Save everything else on main process
    kwargs = {
        "dataset_name": script_args.dataset_name,
        "tags": ["GRPOTrainer-R1"],
    }
    if trainer.accelerator.is_main_process:
        trainer.create_model_card(**kwargs)
        # Restore k,v cache for fast inference
        trainer.model.config.use_cache = True
        trainer.model.config.save_pretrained(training_args.output_dir)

script_config = {
    "dataset_name": "AI-MO/NuminaMath-TIR",
    "dataset_config": "default",
    "reward_funcs": [
        "accuracy",
        "format",
        "reasoning_steps",
    ]
}

training_config = {
    "output_dir": "output/GRPO-R1-1.5B", # 模型輸出目錄
    "overwrite_output_dir": True, # 是否覆蓋輸出目錄
    "do_eval": True, # 是否進行評估
    "eval_strategy": "steps", # 評估策略，按步數進行評估
    "eval_steps": 100, # 每100步進行一次評估
    "per_device_train_batch_size": 4, # 每個設備上的訓練批次大小
    "per_device_eval_batch_size": 4, # 每個設備上的評估批次大小
    "gradient_accumulation_steps": 8, # 梯度累積步數
    "learning_rate": 1.0e-06, # 學習率
    "num_train_epochs": 1.0, # 訓練的總輪數
    "max_steps": -1, # 最大訓練步數，-1表示不限制
    "lr_scheduler_type": "cosine", # 學習率調度器類型，使用余弦退火
    "warmup_ratio": 0.1, # 預熱比例
    "log_level": "info", # 日志記錄級別
    "logging_strategy": "steps", # 日志記錄策略，按步數記錄
    "logging_steps": 100, # 每100步記錄一次日志
    "save_strategy": "no", # 保存策略，不保存
    "seed": 42, # 隨機種子
    "bf16": True, # 是否使用bfloat16精度
    "gradient_checkpointing": True, # 是否使用梯度檢查點
    "gradient_checkpointing_kwargs": {
        "use_reentrant": False# 梯度檢查點的額外參數，是否使用reentrant模式
    },
    "max_prompt_length": 128, # 最大提示長度
    "num_generations": 4, # 生成的數量
    "max_completion_length": 256, # 最大完成長度
    "use_vllm": True, # 是否使用vLLM
    "vllm_device": "auto", # vLLM設備，自動選擇
    "vllm_gpu_memory_utilization": 0.8, # vLLM GPU內存利用率
    "resume_from_checkpoint": "output/GRPO-R1-1.5B", # 恢復檢查點，如果沒有latest文件，需要添加latest文件類似`global_step9055`
}

model_config = {
    "model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
    "model_revision": "main",
    "torch_dtype": "bfloat16",
    "attn_implementation": "flash_attention_2",
}

if __name__ == "__main__":
    script_args = R1GRPOScriptArguments(**script_config)
    training_args = R1GRPOConfig(**training_config)
    model_args = ModelConfig(**model_config)
    main(script_args, training_args, model_args)