Instructions to use open-r1/OlympicCoder-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-r1/OlympicCoder-32B with Transformers:

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

pipe = pipeline("text-generation", model="open-r1/OlympicCoder-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-r1/OlympicCoder-32B")
model = AutoModelForCausalLM.from_pretrained("open-r1/OlympicCoder-32B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use open-r1/OlympicCoder-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-r1/OlympicCoder-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-r1/OlympicCoder-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-r1/OlympicCoder-32B

SGLang

How to use open-r1/OlympicCoder-32B 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 "open-r1/OlympicCoder-32B" \
    --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": "open-r1/OlympicCoder-32B",
		"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 "open-r1/OlympicCoder-32B" \
        --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": "open-r1/OlympicCoder-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-r1/OlympicCoder-32B with Docker Model Runner:
```
docker model run hf.co/open-r1/OlympicCoder-32B
```

lewtun HF Staff commited on Mar 31

Commit

fcc4dc5

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1 Parent(s): 34113ae

Update README.md

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@@ -91,4 +91,13 @@ The following hyperparameters were used during training on 16 H100 nodes:
 - lr_scheduler_type: cosine_with_min_lr
 - min_lr_rate: 0.1
 - lr_scheduler_warmup_ratio: 0.03
-- num_epochs: 10.0

 - lr_scheduler_type: cosine_with_min_lr
 - min_lr_rate: 0.1
 - lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 10.0
+@misc{penedo2025olympiccoder,
+      title={OlympicCoder},
+      author={Guilherme Penedo and Anton Lozhkov and Hynek Kydlíček and Loubna Ben Allal and Edward Beeching and Agustín Piqueres Lajarín and Quentin Gallouédec and Nathan Habib and Lewis Tunstall and Leandro von Werra},
+      year={2025},
+      publisher = {Hugging Face},
+      journal = {Hugging Face repository},
+      howpublished = {\url{https://huggingface.co/open-r1/OlympicCoder-32B}}
+}