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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

OpenMath-Nemotron-14B-Kaggle / entrypoint.sh

igitman

inference_endpoint (#2)

b1a665d verified over 1 year ago

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704 Bytes

	#!/bin/bash
	set -e

	# Default environment variables
	export MODEL_PATH=${MODEL_PATH:-"/repository"}

	echo "Starting NeMo Skills inference endpoint..."
	echo "Model path: $MODEL_PATH"

	# Function to handle cleanup on exit
	cleanup() {
	echo "Cleaning up processes..."
	kill $(jobs -p) 2>/dev/null \|\| true
	wait
	}
	trap cleanup EXIT

	# Start the model server in the background
	echo "Starting model server..."
	ns start_server \
	--model="$MODEL_PATH" \
	--server_gpus=2 \
	--server_type=vllm \
	--with_sandbox &

	# Start the HTTP endpoint
	echo "Starting HTTP endpoint on port 80..."
	python /usr/local/endpoint/server.py &

	# Wait for both processes
	echo "Both servers started. Waiting..."
	wait