Instructions to use second-state/Nemotron-Mini-4B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
NeMo
How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```

How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/Nemotron-Mini-4B-Instruct-GGUF",
	filename="NVIDIA-Nemotron-Nano-9B-v2-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/Nemotron-Mini-4B-Instruct-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 second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

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 second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

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 second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/Nemotron-Mini-4B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for second-state/Nemotron-Mini-4B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/Nemotron-Mini-4B-Instruct-GGUF to start chatting

How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/Nemotron-Mini-4B-Instruct-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 second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

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 second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

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 "second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Nemotron-Mini-4B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Nemotron-Mini-4B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-Mini-4B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Nemotron-Mini-4B-Instruct-GGUF / README.md

apepkuss79

Update README.md

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	---
	base_model: nvidia/Nemotron-Mini-4B-Instruct
	license: other
	license_name: nvidia-community-model-license
	inference: false
	model_creator: nvidia
	model_name: Nemotron-Mini-4B-Instruct
	quantized_by: Second State Inc.
	library_name: nemo
	---

	<!-- header start -->
	<!-- 200823 -->
	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
	<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
	<!-- header end -->

	# Nemotron-Mini-4B-Instruct-GGUF

	## Original Model

	[nvidia/Nemotron-Mini-4B-Instruct](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct)

	## Run with LlamaEdge

	- LlamaEdge version: coming soon

	<!-- - LlamaEdge version: [v0.14.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.14.2) and above -->

	- Prompt template

	- Prompt type: `nemotron-chat`

	- Prompt string

	```text
	<extra_id_0>System
	{system_message}
	<extra_id_1>User
	{user_message_1}<extra_id_1>Assistant
	{assistant_message_1}
	<extra_id_1>User
	{user_message_2}<extra_id_1>Assistant
	{assistant_message_2}
	<extra_id_1>User
	{user_message_3}
	<extra_id_1>Assistant\n
	```

	<!-- - Tool use

	```text
	<extra_id_0>System
	{system prompt}

	<tool> ... </tool>
	<context> ... </context>

	<extra_id_1>User
	{prompt}
	<extra_id_1>Assistant
	<toolcall> ... </toolcall>
	<extra_id_1>Tool
	{tool response}
	<extra_id_1>Assistant\n
	``` -->

	- Context size: `4096`

	- Run as LlamaEdge service

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Nemotron-Mini-4B-Instruct-Q5_K_M.gguf \
	llama-api-server.wasm \
	--prompt-template nemotron-chat \
	--ctx-size 4096 \
	--model-name Nemotron-Mini-4B-Instruct
	```

	- Run as LlamaEdge command app

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Nemotron-Mini-4B-Instruct-Q5_K_M.gguf \
	llama-chat.wasm \
	--prompt-template nemotron-chat \
	--ctx-size 4096
	```

	## Quantized GGUF Models

	\| Name \| Quant method \| Bits \| Size \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [Nemotron-Mini-4B-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q2_K.gguf) \| Q2_K \| 2 \| 3.35 GB\| smallest, significant quality loss - not recommended for most purposes \|
	\| [Nemotron-Mini-4B-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 4.69 GB\| small, substantial quality loss \|
	\| [Nemotron-Mini-4B-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 4.32 GB\| very small, high quality loss \|
	\| [Nemotron-Mini-4B-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.90 GB\| very small, high quality loss \|
	\| [Nemotron-Mini-4B-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q4_0.gguf) \| Q4_0 \| 4 \| 5.04 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [Nemotron-Mini-4B-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 5.33 GB\| medium, balanced quality - recommended \|
	\| [Nemotron-Mini-4B-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 5.07 GB\| small, greater quality loss \|
	\| [Nemotron-Mini-4B-Instruct-Q5_0.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q5_0.gguf) \| Q5_0 \| 5 \| 6.11 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [Nemotron-Mini-4B-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 6.26 GB\| large, very low quality loss - recommended \|
	\| [Nemotron-Mini-4B-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 6.11 GB\| large, low quality loss - recommended \|
	\| [Nemotron-Mini-4B-Instruct-Q6_K.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q6_K.gguf) \| Q6_K \| 6 \| 7.25 GB\| very large, extremely low quality loss \|
	\| [Nemotron-Mini-4B-Instruct-Q8_0.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-Q8_0.gguf) \| Q8_0 \| 8 \| 9.38 GB\| very large, extremely low quality loss - not recommended \|
	\| [Nemotron-Mini-4B-Instruct-f16.gguf](https://huggingface.co/second-state/Nemotron-Mini-4B-Instruct-GGUF/blob/main/Nemotron-Mini-4B-Instruct-f16.gguf) \| f16 \| 16 \| 17.7 GB\| \|

	Quantized with llama.cpp b3751