Instructions to use open-r1/OlympicCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-r1/OlympicCoder-7B with Transformers:

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

pipe = pipeline("text-generation", model="open-r1/OlympicCoder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-r1/OlympicCoder-7B")
model = AutoModelForCausalLM.from_pretrained("open-r1/OlympicCoder-7B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use open-r1/OlympicCoder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-r1/OlympicCoder-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-r1/OlympicCoder-7B

SGLang

How to use open-r1/OlympicCoder-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-r1/OlympicCoder-7B with Docker Model Runner:
```
docker model run hf.co/open-r1/OlympicCoder-7B
```

Unable to run.. crashes with error after loading model

by jdc4429 - opened Mar 24, 2025

Discussion

jdc4429

Mar 24, 2025

Here is the error message:

Traceback (most recent call last):

File "D:\text-generation-webui\modules\ui_model_menu.py", line 231, in load_model_wrapper

shared.model, shared.tokenizer = load_model(selected_model, loader)

                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\modules\models.py", line 101, in load_model

tokenizer = load_tokenizer(model_name, model)

        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\modules\models.py", line 123, in load_tokenizer

tokenizer = AutoTokenizer.from_pretrained(

        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\installer_files\env\Lib\site-packages\transformers\models\auto\tokenization_auto.py", line 896, in from_pretrained

return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)

   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\installer_files\env\Lib\site-packages\transformers\tokenization_utils_base.py", line 2291, in from_pretrained

return cls._from_pretrained(

   ^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\installer_files\env\Lib\site-packages\transformers\tokenization_utils_base.py", line 2525, in _from_pretrained

tokenizer = cls(*init_inputs, **init_kwargs)

        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "D:\text-generation-webui\installer_files\env\Lib\site-packages\transformers\models\qwen2\tokenization_qwen2_fast.py", line 120, in init

super().init(
File "D:\text-generation-webui\installer_files\env\Lib\site-packages\transformers\tokenization_utils_fast.py", line 115, in init

fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)

             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Exception: data did not match any variant of untagged enum ModelWrapper at line 757443 column 3

jdc4429

Mar 24, 2025

Update seems to have fixed it... Why are the VRAM requirements so high? Can't even run past 6000 context with 24gb + 8gb cards.

jdc4429 changed discussion status to closed Mar 24, 2025

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