Instructions to use cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct

SGLang

How to use cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/cmcheng/DeepMath-GRPO_Qwen2.5-0.5B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GRPO 微调 Qwen2.5-0.5B-Instruct

训练环境：
- 显卡：2 * NVIDIA 4080 32GB
- 加速框架：DeepSpeed，采用 bf16 混合精度训练
训练集：zwhe99/DeepMath-103K
训练数据量：97870
测试数据量：5152
验证集选取：从测试集随机抽取 100 条
最大迭代步数限制： 5000
训练参数：

grpo_config = GRPOConfig(
        # ---- 基础配置 ----
        output_dir="./deepmath_grpo_output",
        save_strategy='best',
        save_total_limit=5,
        #save_steps=100,   
        # ---- 批次大小 ----
        per_device_train_batch_size=4,          # 每设备批次大小
        per_device_eval_batch_size=4,
        gradient_accumulation_steps=4,           # 梯度累积步数
        # 有效批次大小 = 4 * 2 * 8 GPUs = 64（与论文 512 有差距，可根据硬件调整）
        
        # ---- 训练步数 ----
        max_steps=1000,                          # 论文中 DeepMath-Zero 训练 500 步
        #num_train_epochs=1,
        # ---- 推理框架配置
        use_vllm=True,
        vllm_gpu_memory_utilization=0.3,

        # 评估策略
        eval_strategy='steps',
        eval_steps=50,
        metric_for_best_model="eval_reward",
        greater_is_better=True,
        logging_strategy='epoch',
        logging_dir="train_logs/",
        load_best_model_at_end=True,

        # ---- 学习率 ----
        learning_rate=1e-6,                     # 论文 Table 5: lr=1e-6
        
        # ---- GRPO 特有参数 ----
        num_generations=settings.GROUP_SIZE_TRAIN,  # 4                    
        num_generations_eval=settings.GROUP_SIZE_EVAL,  # 4
        generation_batch_size=4,                # 生成批次大小
        max_completion_length=2048,             # 最大生成长度（论文推理时为 32768，训练时 2048）
        loss_type='grpo', # 使用标准 GRPO 算法训练
        
        # ---- KL 散度控制 ----
        beta=0.001,                          # 论文 Table 5: kl_coef=1e-3
        
        # ---- 裁剪参数 ----
        epsilon=0.2,                     # 论文 Table 5: clip_ratio_low=0.2
        epsilon_high=0.28,                   # 论文 Table 5: clip_ratio_high=0.28
        
        # ---- 温度参数 ----
        temperature=1.0,                        # 论文 Table 5: temperature=1.0 (Zero RL)
    
        # ---- 内存优化 ----
        bf16=torch.cuda.is_bf16_supported(),
        fp16=not torch.cuda.is_bf16_supported(),
        gradient_checkpointing=True,
        
        # ---- 报告与日志 ----
        report_to="tensorboard",                       # 可改为 "wandb" 启用 wandb 日志
        run_name="deepmath-grpo-qwen-0.5b-instruct",

        # 随机种子
        seed=settings.SEED
    )

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Safetensors

Model size

0.6B params

Tensor type

BF16

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Quantizations

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