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
```

Update model card with revised validation accuracy formatting

by geniehuang - opened Sep 5

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-4

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -71,11 +71,11 @@ The training data consists of **57,046 examples** from the DeepMath Level 6 trai
 - **Training-time validation datasets:** AIME25, AMC22–23, AIME24
 - **Validation questions:** 143
-## Validation Results
-- **AIME 2025:** accuracy (`avg@16`): **21.25%**
-- **AMC 2022–2023:** accuracy (`avg@16`): **60.77%**
-- **AIME 2024:** accuracy (`avg@16`): **23.54%**
 ## Usage

 - **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