Instructions to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

SGLang

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF 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 "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF" \
    --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": "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF",
		"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 "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF" \
        --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": "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-NVFP4-GGUF:NVFP4" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 - GGUF

This repository contains GGUF format model files for WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-GGUF (quantized to NVFP4).

These files were converted using llama.cpp.

Available Files

Filename	Quant Method	Description
`nemotron-3.5-lightning-30b-a3b-nvfp4.gguf`	NVFP4	Main model weights in NVIDIA FP4 format
`nemotron-3.5-lightning-30b-a3b-dflash-nvfp4.gguf`	NVFP4	DFlash speculative decoding draft model

Model Summary

Total Parameters	30B (3B active)
Architecture	MoE — Mamba-2 + MoE + Attention hybrid
Context Length	Up to 1M tokens (256K native default)
Supported Languages	English (and coding languages), Spanish, French, German, Italian, Japanese
Speculative Decoding	DSpark, DFlash, MTP (Multi-Token Prediction)
Reasoning Mode	Configurable on/off via chat template (`enable_thinking=True/False`)
Recommended Sampling	Temperature 1.0, Top_P 0.95
License	OpenMDW License Agreement, version 1.1
Original Model	WhiskyAKM/Nemotron-3.5-Lightning-30B-A3B-GGUF

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - May 2026

Data Freshness:

The pre-training data has a cutoff date of September 2025.
The post-training data has a cutoff date of May 2026.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Description

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is a large language model (LLM) trained and quantized by NVIDIA.

The model employs a hybrid Mixture-of-Experts architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The Lightning 3.5 model is released alongside speculative decoding methods (DSpark, DFlash, MTP) for faster text generation. The model has 3B active parameters and 30B parameters in total.

This model is ready for commercial use under the OpenMDW-1.1 license.

Usage with llama.cpp

CLI / llama-cli

Reasoning ON (default):

llama-cli \
  -m nemotron-3.5-lightning-30b-a3b-nvfp4.gguf \
  --jinja \
  --chat-template-file chat_template.jinja \
  -p "Write a Python function to compute Fibonacci numbers." \
  --temp 1.0 --top-p 0.95 \
  -ngl 99

llama-server

Start the OpenAI-compatible server:

llama-server \
  -m nemotron-3.5-lightning-30b-a3b-nvfp4.gguf \
  --temp 1.0 --top-p 0.95 \
  -np 1 \
  -c 40960 \
  --port 8000 \
  -ngl 99 \
  -fa on \
  --jinja \
  --chat-template-file chat_template.jinja \
  --no-webui \
  --fit off

With DFlash Speculative Decoding

Accelerate token generation using the DFlash draft model:

llama-server \
  -m nemotron-3.5-lightning-30b-a3b-nvfp4.gguf \
  -md nemotron-3.5-lightning-30b-a3b-dflash-nvfp4.gguf \
  --draft-max 6 \
  --temp 1.0 --top-p 0.95 \
  -np 1 \
  -c 40960 \
  --port 8000 \
  -ngl 99 \
  -ngld 99 \
  -fa on \
  --jinja \
  --chat-template-file chat_template.jinja \
  --no-webui \
  --fit off

API Client Example (OpenAI SDK)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

# Reasoning ON (default)
response = client.chat.completions.create(
    model="nemotron-3.5-lightning-30b-a3b",
    messages=[{"role": "user", "content": "Write a haiku about GPUs"}],
    max_tokens=4096,
    temperature=1.0,
    top_p=0.95,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(response.choices[0].message.content)

# Reasoning OFF (direct answer)
response = client.chat.completions.create(
    model="nemotron-3.5-lightning-30b-a3b",
    messages=[{"role": "user", "content": "What is the capital of Japan?"}],
    max_tokens=128,
    temperature=1.0,
    top_p=0.95,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)

Benchmarks

Reasoning Benchmark Evaluations

Task	Nemotron-3.5-Lightning-30B-A3B-BF16	Nemotron-3.5-Lightning-30B-A3B-NVFP4
General Knowledge
MMLU Pro	81.94	81.62
AA-Omniscience	17.50	16.63
Reasoning
GPQA Diamond (no tools)	75.44	75.57
HLE (text-only, no tools)	11.72	10.47
SciCode	32.60	31.38
Coding & Agentic
SWE-bench Verified	51.56	52.80
SWE-bench Multilingual	39.33	36.47
Terminal-Bench 2.1	24.58	23.46
PinchBench	85.37	83.43
BrowseComp	36.97	36.81
τ³-bench (Banking)	9.28	9.48
GDPval-AA-V2	832	865
Instruction Following
IFBench (loose)	71.88	72.88
Long Context
AA-LCR	52.00	49.19