Instructions to use nvidia/OpenCodeReasoning-Nemotron-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenCodeReasoning-Nemotron-14B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/OpenCodeReasoning-Nemotron-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenCodeReasoning-Nemotron-14B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenCodeReasoning-Nemotron-14B")
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 nvidia/OpenCodeReasoning-Nemotron-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/OpenCodeReasoning-Nemotron-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/OpenCodeReasoning-Nemotron-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/OpenCodeReasoning-Nemotron-14B

SGLang

How to use nvidia/OpenCodeReasoning-Nemotron-14B 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 "nvidia/OpenCodeReasoning-Nemotron-14B" \
    --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": "nvidia/OpenCodeReasoning-Nemotron-14B",
		"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 "nvidia/OpenCodeReasoning-Nemotron-14B" \
        --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": "nvidia/OpenCodeReasoning-Nemotron-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/OpenCodeReasoning-Nemotron-14B with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenCodeReasoning-Nemotron-14B
```

wasiuddina commited on Apr 24, 2025

Commit

77881cb

verified ·

1 Parent(s): a9c5889

Update README.md

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@@ -10,11 +10,11 @@ tags:
   - pytorch
 ---
-# OpenCode-Nemotron-14B Overview
 ## Description
-OpenCode-Nemotron-14B is a large language model (LLM) which is a derivative of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (AKA the *reference model*).
 It is a reasoning model that is post trained for reasoning while code generation. The model supports a context length of 32K tokens.
 This model is ready for commercial use.
@@ -29,7 +29,7 @@ Global<br>
 This model is intended for developers and researchers building LLMs. <br>
 ### Release Date:  <br>
-Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCode-Nemotron-14B/ <br>
 ## References
@@ -70,13 +70,13 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 ## Training Dataset
-The training corpus for OpenCode-Nemotron-14B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>
 ## Evaluation Dataset
-We used the datasets listed in the next section to evaluate OpenCodeReasoning-32B. <br>
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>

   - pytorch
 ---
+# OpenCodeReasoning-Nemotron-14B Overview
 ## Description
+OpenCodeReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (AKA the *reference model*).
 It is a reasoning model that is post trained for reasoning while code generation. The model supports a context length of 32K tokens.
 This model is ready for commercial use.
 This model is intended for developers and researchers building LLMs. <br>
 ### Release Date:  <br>
+Huggingface [04/25/2025] via https://huggingface.co/nvidia/OpenCodeReasoning-Nemotron-14B/ <br>
 ## References
 ## Training Dataset
+The training corpus for OpenCodeReasoning-Nemotron-14B is [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, which is composed of competitive programming questions and DeepSeek-R1 generated responses.
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>
 ## Evaluation Dataset
+We used the datasets listed in the next section to evaluate OpenCodeReasoning-Nemotron-14B. <br>
 * Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
 * Data Labeling Method: Hybrid: Automated, Human, Synthetic <br>