Instructions to use Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4", trust_remote_code=True, 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4

SGLang

How to use Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 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 "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4" \
    --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": "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4",
		"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 "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4" \
        --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": "Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4
```

NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 / nano_v3_reasoning_parser.py

Arsh9210

Bulk upload of 20 small files

d256296 verified about 2 months ago

Raw

History Blame Contribute Delete

799 Bytes

	from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
	from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser


	@ReasoningParserManager.register_module("nano_v3")
	class NanoV3ReasoningParser(DeepSeekR1ReasoningParser):
	def extract_reasoning(self, model_output, request):
	reasoning_content, final_content = super().extract_reasoning(
	model_output, request
	)
	if (
	hasattr(request, "chat_template_kwargs")
	and request.chat_template_kwargs
	and request.chat_template_kwargs.get("enable_thinking") is False
	and final_content is None
	):
	reasoning_content, final_content = final_content, reasoning_content

	return reasoning_content, final_content