Instructions to use nvidia/OpenCodeReasoning-Nemotron-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/OpenCodeReasoning-Nemotron-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/OpenCodeReasoning-Nemotron-7B")
model = AutoModelForCausalLM.from_pretrained("nvidia/OpenCodeReasoning-Nemotron-7B")
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 nvidia/OpenCodeReasoning-Nemotron-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nvidia/OpenCodeReasoning-Nemotron-7B

SGLang

How to use nvidia/OpenCodeReasoning-Nemotron-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 "nvidia/OpenCodeReasoning-Nemotron-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": "nvidia/OpenCodeReasoning-Nemotron-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 "nvidia/OpenCodeReasoning-Nemotron-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": "nvidia/OpenCodeReasoning-Nemotron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/OpenCodeReasoning-Nemotron-7B with Docker Model Runner:
```
docker model run hf.co/nvidia/OpenCodeReasoning-Nemotron-7B
```

wasiuddina commited on Apr 16, 2025

Commit

54fb6ef

verified ·

1 Parent(s): 8478d18

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -10,11 +10,11 @@ tags:
   - pytorch
 ---
-# OpenCodeReasoning-Distill-Qwen-7B-Instruct
 ## Model Overview
-OpenCodeReasoning-Distill-Qwen-7B-Instruct is a large language model (LLM) which is a derivative of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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.
@@ -49,7 +49,7 @@ Developers designing AI-powered code generation applications. Also suitable for
 ## Intended use
-OpenCodeReasoning-Distill-Qwen-7B-Instruct is a competitive code generation focused reasoning and chat model intended to be used in English.
 ## Input
 - **Input Type:** Text
@@ -87,7 +87,7 @@ Example:
 import torch
 import transformers
-model_id = "nvidia/OpenCodeReasoning-Distill-Qwen-7B-Instruct"
 model_kwargs = {"torch_dtype": torch.bfloat16, "trust_remote_code": True, "device_map": "auto"}
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
 tokenizer.pad_token_id = tokenizer.eos_token_id
@@ -139,7 +139,7 @@ Data Labeling for Evaluation Datasets:
 | Models | Pass@1 |
 |:--------------|:------------|
 | R1-Distill-Qwen-7B | 37.6 |
-| OpenCodeReasoning-Distill-Qwen-7B-Instruct | 51.3 |
 User Prompt Template (without starter code):

   - pytorch
 ---
+# OpenCodeReasoning-7B
 ## Model Overview
+OpenCodeReasoning-7B is a large language model (LLM) which is a derivative of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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.
 ## Intended use
+OpenCodeReasoning-7B is a competitive code generation focused reasoning and chat model intended to be used in English.
 ## Input
 - **Input Type:** Text
 import torch
 import transformers
+model_id = "nvidia/OpenCodeReasoning-7B"
 model_kwargs = {"torch_dtype": torch.bfloat16, "trust_remote_code": True, "device_map": "auto"}
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
 tokenizer.pad_token_id = tokenizer.eos_token_id
 | Models | Pass@1 |
 |:--------------|:------------|
 | R1-Distill-Qwen-7B | 37.6 |
+| OpenCodeReasoning-7B | 51.3 |
 User Prompt Template (without starter code):