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

Libraries

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

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Cascade-14B-Thinking")
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("nvidia/Nemotron-Cascade-14B-Thinking")
model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Cascade-14B-Thinking", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Nemotron-Cascade-14B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Cascade-14B-Thinking"
# 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/Nemotron-Cascade-14B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

zihanliu commited on Dec 12, 2025

Commit

0b45581

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@@ -18,7 +18,7 @@ tags:
 ---
-# Cascade RL: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models
 <p align="center">
@@ -31,6 +31,8 @@ tags:
 <img src="fig/nemotron-cascade-14b-thinking-results.png" alt="main_fig" style="width: 1000px; max-width: 100%;" />
 We're excited to introduce [Nemotron-Cascade-14B-Thinking](https://huggingface.co/nvidia/Nemotron-Cascade-14B-Thinking), a powerful general-purpose model trained through sequential and domain-wise reinforcement learning. Nemotron-Cascade-14B-Thinking is post-trained from the [Qwen3-14B Base](https://huggingface.co/Qwen/Qwen3-14B-Base) model, and it achieves best-in-class performance across a wide range of benchmarks. Different from [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), Nemotron-Cascade-14B-Thinking is designed exclusively for the ***thinking*** mode.

 ---
+# Nemotron-Cascade-14B-Thinking
 <p align="center">
 <img src="fig/nemotron-cascade-14b-thinking-results.png" alt="main_fig" style="width: 1000px; max-width: 100%;" />
+## Introduction
 We're excited to introduce [Nemotron-Cascade-14B-Thinking](https://huggingface.co/nvidia/Nemotron-Cascade-14B-Thinking), a powerful general-purpose model trained through sequential and domain-wise reinforcement learning. Nemotron-Cascade-14B-Thinking is post-trained from the [Qwen3-14B Base](https://huggingface.co/Qwen/Qwen3-14B-Base) model, and it achieves best-in-class performance across a wide range of benchmarks. Different from [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), Nemotron-Cascade-14B-Thinking is designed exclusively for the ***thinking*** mode.