GGUF
quantized
apex
Mixture of Experts
mixture-of-experts
nvidia
nemotron
mamba
hybrid
conversational
Instructions to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF", filename="Nemotron-3-Nano-30B-A3B-APEX-Balanced.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 mudler/Nemotron-3-Nano-30B-A3B-APEX-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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF # Run inference directly in the terminal: llama cli -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF # Run inference directly in the terminal: llama cli -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF # Run inference directly in the terminal: ./llama-cli -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
Use Docker
docker model run hf.co/mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
- LM Studio
- Jan
- Ollama
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with Ollama:
ollama run hf.co/mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
- Unsloth Studio
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-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 mudler/Nemotron-3-Nano-30B-A3B-APEX-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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF to start chatting
- Pi
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
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": "mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
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 mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with Docker Model Runner:
docker model run hf.co/mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
- Lemonade
How to use mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mudler/Nemotron-3-Nano-30B-A3B-APEX-GGUF
Run and chat with the model
lemonade run user.Nemotron-3-Nano-30B-A3B-APEX-GGUF-{{QUANT_TAG}}List all available models
lemonade list
Update support banner: orange GPU-rental ask + thanks HF for storage
Browse files
README.md
CHANGED
|
@@ -16,6 +16,8 @@ tags:
|
|
| 16 |
|
| 17 |
|
| 18 |
|
|
|
|
|
|
|
| 19 |
<div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
|
| 20 |
<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
|
| 21 |
<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
|
|
|
|
| 16 |
|
| 17 |
|
| 18 |
|
| 19 |
+
|
| 20 |
+
<!-- apex-banner-v2 -->
|
| 21 |
<div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
|
| 22 |
<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
|
| 23 |
<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
|