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 from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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

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

Configuration Parsing Warning:Invalid JSON for config file config.json

Nemotron-Mini-4B-Instruct-GGUF

Original Model

nvidia/Nemotron-Mini-4B-Instruct

Run with LlamaEdge

LlamaEdge version: coming soon

Prompt template

Prompt type: nemotron-chat

Prompt string

<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

<extra_id_0>System
{system_message}
<tool> {tool_1} </tool>
<tool> {tool_2} </tool>


<extra_id_1>User
{user_message_1}<extra_id_1>Assistant
<toolcall> {tool_call_message} </toolcall>
<extra_id_1>Tool
{tool_result_message}
<extra_id_1>Assistant\n

Context size: 4096

Run as LlamaEdge service

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

Tool use

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

Run as LlamaEdge command app

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	Q2_K	2	3.35 GB	smallest, significant quality loss - not recommended for most purposes
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	Q3_K_M	3	4.32 GB	very small, high quality loss
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	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	Q4_K_M	4	5.33 GB	medium, balanced quality - recommended
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	Q5_0	5	6.11 GB	legacy; medium, balanced quality - prefer using Q4_K_M
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	Q5_K_S	5	6.11 GB	large, low quality loss - recommended
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	Q8_0	8	9.38 GB	very large, extremely low quality loss - not recommended
Nemotron-Mini-4B-Instruct-f16.gguf	f16	16	17.7 GB

Quantized with llama.cpp b3751

Downloads last month: 327

GGUF

Model size

4B params

Architecture

nemotron

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for second-state/Nemotron-Mini-4B-Instruct-GGUF

Base model

nvidia/Nemotron-Mini-4B-Instruct

Quantized

(26)

this model

Collection including second-state/Nemotron-Mini-4B-Instruct-GGUF

Tool Use

Collection

LlamaEdge compatible quants for tool-use models. • 11 items • Updated Oct 16, 2024