Instructions to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/Nemotron-3-Nano-30B-A3B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unsloth/Nemotron-3-Nano-30B-A3B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/Nemotron-3-Nano-30B-A3B-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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/Nemotron-3-Nano-30B-A3B-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": "unsloth/Nemotron-3-Nano-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/Nemotron-3-Nano-30B-A3B-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 "unsloth/Nemotron-3-Nano-30B-A3B-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": "unsloth/Nemotron-3-Nano-30B-A3B-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 "unsloth/Nemotron-3-Nano-30B-A3B-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": "unsloth/Nemotron-3-Nano-30B-A3B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with Ollama:
ollama run hf.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/Nemotron-3-Nano-30B-A3B-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 unsloth/Nemotron-3-Nano-30B-A3B-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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/Nemotron-3-Nano-30B-A3B-GGUF to start chatting
- Pi
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
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": "unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
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 "unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL" \ --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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/Nemotron-3-Nano-30B-A3B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Nemotron-3-Nano-30B-A3B-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use unsloth/Nemotron-3-Nano-30B-A3B-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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
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 unsloth/Nemotron-3-Nano-30B-A3B-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
| {{ if .Tools }}<|im_start|>system | |
| # Tools | |
| You have access to the following functions: | |
| <tools>{{ range .Tools }} | |
| <function> | |
| <name>{{ .Function.Name }}</name>{{ if .Function.Description }} | |
| <description>{{ .Function.Description }}</description>{{ end }} | |
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| <required>{{ json .Function.Parameters.Required }}</required>{{ end }} | |
| </parameters> | |
| </function> | |
| {{ end }}</tools> | |
| If you choose to call a function ONLY reply in the following format with NO suffix: | |
| <tool_call> | |
| <function=example_function_name> | |
| <parameter=example_parameter_1> | |
| value_1 | |
| </parameter> | |
| <parameter=example_parameter_2> | |
| This is the value for the second parameter | |
| that can span | |
| multiple lines | |
| </parameter> | |
| </function> | |
| </tool_call> | |
| <IMPORTANT> | |
| Reminder: | |
| - Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags | |
| - Required parameters MUST be specified | |
| - You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after | |
| - If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls | |
| </IMPORTANT>{{ if .System }}{{ .System }}{{ end }}<|im_end|> | |
| {{ else }}<|im_start|>system | |
| {{ if .System }}{{ .System }}{{ end }}<|im_end|> | |
| {{ end }}{{ range $i, $_ := .Messages }}{{ $last := eq (len (slice $.Messages $i)) 1 }}{{ if eq .Role "user" }}<|im_start|>user | |
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| {{ if eq (printf "%T" $v) "string" }}{{ $v }}{{ else }}{{ json $v }}{{ end }} | |
| </parameter> | |
| {{ end }}</function> | |
| </tool_call>{{ end }} | |
| <|im_end|> | |
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| <tool_response> | |
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| </tool_response> | |
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| <think> | |
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