Instructions to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF",
	filename="nemotron-cascade-30b-Q5_1.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
# Run inference directly in the terminal:
llama-cli -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
# Run inference directly in the terminal:
llama-cli -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
# Run inference directly in the terminal:
./llama-cli -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

Use Docker

docker model run hf.co/AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

LM Studio
Jan
Ollama
How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with Ollama:
```
ollama run hf.co/AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
```

Unsloth Studio new

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF to start chatting

Pi new

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

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": "AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

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 AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

Run Hermes

hermes

Docker Model Runner
How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with Docker Model Runner:
```
docker model run hf.co/AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1
```

Lemonade

How to use AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF:Q5_1

Run and chat with the model

lemonade run user.Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF-Q5_1

List all available models

lemonade list

Nemotron-Cascade-2-30B-A3B — Q5_1 GGUF

GGUF quantization of nvidia/Nemotron-Cascade-2-30B-A3B.

Architecture: Hybrid Attention + Mamba (SSM) + MoE — 30B total parameters, 3B active
Quantization: Q5_1 (uniform 5-bit with delta and min per block)

Quantization commands

# Convert HF model to GGUF (bf16)
python llama.cpp/convert_hf_to_gguf.py \
  nvidia/Nemotron-Cascade-2-30B-A3B \
  --outfile nemotron-cascade-30b-bf16.gguf \
  --outtype bf16

# Quantize to Q5_1
llama-quantize nemotron-cascade-30b-bf16.gguf \
  nemotron-cascade-30b-Q5_1.gguf Q5_1

Usage

Load in LM Studio, llama.cpp, or any GGUF-compatible runtime.

Downloads last month: 15

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

5-bit

Inference Providers NEW

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Model tree for AdrienBrault/Nemotron-Cascade-2-30B-A3B-Q5_1-GGUF

Base model

nvidia/Nemotron-Cascade-2-30B-A3B

Quantized

(30)

this model