Instructions to use ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO")
model = AutoModelForCausalLM.from_pretrained("ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO

SGLang

How to use ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO 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 "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO" \
    --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": "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO",
		"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 "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO" \
        --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": "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO with Docker Model Runner:
```
docker model run hf.co/ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO
```

DeepMath-103K-Level6-Qwen3-4B-Base-GRPO

DeepMath-103K-Level6-Qwen3-4B-Base-GRPO is a post-trained version of Qwen/Qwen3-4B-Base, trained with Group Relative Policy Optimization (GRPO) on the Level 6 subset of DeepMath-103K for mathematical reasoning.

Training used the verl framework and the training code from Thinking-Space/Rethinking-OPD, with full-parameter actor updates and rule-based math outcome rewards.

Dataset

The training data consists of 57,046 examples from the DeepMath Level 6 training file available in Keven16/G-OPD-Training-Data, originating from zwhe99/DeepMath-103K.

Dataset file: DeepMath-103K/train_filtered_level6.parquet.
System prompt: Please reason step by step, and put your final answer within \boxed{}.
Validation datasets: AIME 2025 (30 questions), AMC 2022–2023 (83 questions), and AIME 2024 (30 questions).

Training Details

Training configuration

Base model: Qwen/Qwen3-4B-Base
Training framework: verl
Algorithm: GRPO
Parameter update: Full-parameter fine-tuning
Rollout engine: vLLM
Context length: 32,768 tokens
Responses per prompt: 8
GRPO outcome weight: 1.0
Prompt length: 1,024 tokens
Response length: 7,168 tokens
Max model length: 32,768 tokens
Rollout temperature: 1.0
Rollout top-p / top-k: 1.0 / -1
Repetition penalty: 1.0
KL loss: Disabled
Format reward: Disabled
Learned reward model: Disabled
Loss aggregation: token-mean
Learning rate: 1e-6
Learning-rate schedule: Constant; no warmup
Weight decay: 0.01
PPO mini-batch size: 64
PPO micro-batch size per GPU: 1
Number of GPUs: 4
Number of epochs: 1
Save frequency: Every 20 steps
Test frequency: Every 20 steps
Validation sampling: 16 responses per prompt; temperature 1.0; top-p 0.95
Validation response length: 31,744 tokens

Dataset

Training dataset: DeepMath-103K Level 6
Training examples: 57,046
Training-time validation datasets: AIME25, AMC22–23, AIME24
Validation questions: 143

Validation Accuracy

AIME 2025 (avg@16): 21.25%
AMC 2022–2023 (avg@16): 60.77%
AIME 2024 (avg@16): 23.54%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ExploreXploitQ/DeepMath-103K-Level6-Qwen3-4B-Base-GRPO"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

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Safetensors

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4B params

Tensor type

BF16

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DeepMath-103K-Level6-Qwen3-4B-Base-GRPO

DeepMath-103K-Level6-Qwen3-4B-Base-GRPO

Dataset

Training Details

Training configuration

Dataset

Validation Accuracy

Usage

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