Instructions to use qubitpage/ornith-9b-classic-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qubitpage/ornith-9b-classic-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="qubitpage/ornith-9b-classic-gguf",
	filename="ornith-9b-classic-q8_0.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 qubitpage/ornith-9b-classic-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 qubitpage/ornith-9b-classic-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf qubitpage/ornith-9b-classic-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qubitpage/ornith-9b-classic-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf qubitpage/ornith-9b-classic-gguf:Q8_0

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 qubitpage/ornith-9b-classic-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf qubitpage/ornith-9b-classic-gguf:Q8_0

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 qubitpage/ornith-9b-classic-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qubitpage/ornith-9b-classic-gguf:Q8_0

Use Docker

docker model run hf.co/qubitpage/ornith-9b-classic-gguf:Q8_0

LM Studio
Jan
Ollama
How to use qubitpage/ornith-9b-classic-gguf with Ollama:
```
ollama run hf.co/qubitpage/ornith-9b-classic-gguf:Q8_0
```

Unsloth Studio

How to use qubitpage/ornith-9b-classic-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 qubitpage/ornith-9b-classic-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 qubitpage/ornith-9b-classic-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qubitpage/ornith-9b-classic-gguf to start chatting

How to use qubitpage/ornith-9b-classic-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qubitpage/ornith-9b-classic-gguf:Q8_0

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": "qubitpage/ornith-9b-classic-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use qubitpage/ornith-9b-classic-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 qubitpage/ornith-9b-classic-gguf:Q8_0

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 qubitpage/ornith-9b-classic-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use qubitpage/ornith-9b-classic-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qubitpage/ornith-9b-classic-gguf:Q8_0

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 "qubitpage/ornith-9b-classic-gguf:Q8_0" \
  --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 qubitpage/ornith-9b-classic-gguf with Docker Model Runner:
```
docker model run hf.co/qubitpage/ornith-9b-classic-gguf:Q8_0
```

Lemonade

How to use qubitpage/ornith-9b-classic-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qubitpage/ornith-9b-classic-gguf:Q8_0

Run and chat with the model

lemonade run user.ornith-9b-classic-gguf-Q8_0

List all available models

lemonade list

Ornith 9B — Classic (Q8_0 GGUF)

Free, local agentic coding model for the Sentinel Coder One Studio VS Code extension. Runs GPU-only on a 12 GB card (e.g. RTX 3060).

Agentic multi-step tool loop (create/edit files, run commands, iterate)
Native tool-calling, coding, and reasoning at 40K context
$0 tokens, fully private — nothing leaves your machine

File

ornith-9b-classic-q8_0.gguf (~8.9 GB, Q8_0) — fits a 12 GB GPU.

Use with Ollama

FROM ./ornith-9b-classic-q8_0.gguf
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>
"""
PARAMETER num_ctx 40960
PARAMETER num_predict 4096
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>

ollama create ornith-9b-classic -f Ornith.Modelfile

Run natively (no Ollama) with NPK

Pack it into the self-contained .npk container and run it inside VS Code on the GPU:

node npk-tool.mjs pack ornith-9b-classic-q8_0.gguf ornith9b.npk \
  --arch qwen2 --name "Ornith 9B — Classic" --ctx 40960 --quant Q8_0

Converter + guide: https://github.com/msrusu87/npk-converter

License: MIT.

Native NPK build (run inside VS Code, no Ollama)

ornith-9b-classic.npk (~8.9 GB) is the same Q8 model pre-packed in the self-contained NeuroPack .npk container. Download it, then in Sentinel Coder One: Add NeuroPack (.npk) Model from Folder and select the folder. It runs GPU-only inside the editor with full agentic / tools / RAG support — no Ollama, no server, no extra installs.

Direct link: https://huggingface.co/qubitpage/ornith-9b-classic-gguf/resolve/main/ornith-9b-classic.npk

(Or pack your own GGUF with the converter: https://github.com/msrusu87/npk-converter)

Downloads last month: 129

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support