Instructions to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JustACluelessKid2/Nemotron-Cascade-8B-GGUF",
	filename="models/Nemotron-Cascade-8B.BF16.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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with Ollama:
```
ollama run hf.co/JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JustACluelessKid2/Nemotron-Cascade-8B-GGUF to start chatting

How to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JustACluelessKid2/Nemotron-Cascade-8B-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": "JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-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 JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M
```

Lemonade

How to use JustACluelessKid2/Nemotron-Cascade-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JustACluelessKid2/Nemotron-Cascade-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemotron-Cascade-8B-GGUF-Q4_K_M

List all available models

lemonade list

Nemotron-Cascade → GGUF converter

Converts the Hugging Face model nvidia/Nemotron-Cascade-8B to GGUF, then quantizes.

Quick start

# Python 3.11 recommended (newer Python should also work)
python3.11 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -r requirements.txt

python scripts/convert_nemotron_to_gguf.py --q8 --q4
# or
python scripts/convert_nemotron_to_gguf.py --all

# preview what would happen (no downloads/build)
python scripts/convert_nemotron_to_gguf.py --all --dry-run

# if you specifically want BF16 as an output (not enabled by --all)
python scripts/convert_nemotron_to_gguf.py --quant BF16

Notes / prerequisites

This script uses llama.cpp to do the actual GGUF conversion + quantization.
You need build tools to compile llama.cpp (CMake + a C++ compiler).
Disk and RAM requirements are significant for 8B-class models.

On Debian/Ubuntu, llama.cpp may require curl dev headers (libcurl4-openssl-dev). If they are missing, the script will automatically retry the build with curl disabled (-DLLAMA_CURL=OFF).

Outputs are written to ./output/ by default.

Quantization recommendation

For most users, prefer Q4_K_M (good quality/size) or Q8_0 (high quality).

Avoid the IQ* quantizations unless you specifically know you need them and you have an imatrix-calibrated workflow. Without an imatrix, IQ* quants can have worse quality-per-bit than K-quants.

The --all flag quantizes to every quant type supported by your local llama-quantize binary, excluding F32 (and COPY).

IQ* quant types are excluded by default; enable them with --iq (includes both IQ and non-IQ), or use --iq-only to generate only the IQ* quants.

MoE-specific quants like MXFP4_MOE are disabled by default; enable them with --moe.

Using an imatrix

If you already have an importance matrix file (imatrix), you can pass it to llama-quantize via:

python scripts/convert_nemotron_to_gguf.py --all --imatrix /path/to/imatrix.dat

This can improve quality for some quant types.

Note: Some quant types may be skipped unless you provide --imatrix, because llama-quantize warns they should not be used without one (e.g. Q2_K_S). If you plan to upload generated .gguf files to Hugging Face, note that the Hub stores large files via Xet on supported repos.

If python3.11 is not available on your system, install it (or run with a newer python3).

Run python convert_nemotron_to_gguf.py --help for options.

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GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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