Instructions to use arunb74/Ornith-1.0-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arunb74/Ornith-1.0-9B-GGUF with Ollama:
```
ollama run hf.co/arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arunb74/Ornith-1.0-9B-GGUF to start chatting

How to use arunb74/Ornith-1.0-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunb74/Ornith-1.0-9B-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": "arunb74/Ornith-1.0-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use arunb74/Ornith-1.0-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

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 "arunb74/Ornith-1.0-9B-GGUF:Q4_K_M" \
  --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 arunb74/Ornith-1.0-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/arunb74/Ornith-1.0-9B-GGUF:Q4_K_M
```

Lemonade

How to use arunb74/Ornith-1.0-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-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 arunb74/Ornith-1.0-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Ornith-1.0-9B-GGUF

GGUF quantizations of Ornith-1.0-9B, converted from the original Hugging Face release by deepreinforce-ai for use with LM Studio, llama.cpp, Ollama, and other GGUF-compatible inference engines.

Base Model

Original Model: deepreinforce-ai/Ornith-1.0-9B
Format: GGUF
Converted by: arunb74

This repository contains only GGUF conversions. All model architecture, weights, tokenizer, and training were created by the original authors.

Available Files

File	Quantization	Size	Recommended RAM
`Ornith-9b-Q4_K_M.gguf`	Q4_K_M	~5.3 GB	8–12 GB

Additional quantizations may be added in future releases.

Supported Software

This GGUF model is compatible with:

LM Studio
llama.cpp
Ollama (via a custom Modelfile)
text-generation-webui
KoboldCpp
Jan
Most applications that support the GGUF format

Using with LM Studio

Open LM Studio.
Go to the Discover tab.
Search for:

arunb74/Ornith-1.0-9B-GGUF

Download Ornith-9b-Q4_K_M.gguf.
Load the model from My Models.
Start chatting.

Alternatively, download the GGUF file manually from this repository and place it in your LM Studio models directory.

Using with llama.cpp

Download the GGUF file and run:

llama-cli \
    -m Ornith-9b-Q4_K_M.gguf \
    -p "Explain reinforcement learning."

Or start an interactive chat:

llama-cli \
    -m Ornith-9b-Q4_K_M.gguf

Using with Ollama

Create a Modelfile:

FROM ./Ornith-9b-Q4_K_M.gguf

Create the model:

ollama create Ornith-9b -f Modelfile

Run it:

ollama run Ornith-9b

Conversion Details

The model was converted using the latest version of llama.cpp.

Conversion:

python convert_hf_to_gguf.py \
    ./Ornith-1.0-9B \
    --outfile Ornith-9b-f16.gguf

Quantization:

llama-quantize \
    Ornith-9b-f16.gguf \
    Ornith-9b-Q4_K_M.gguf \
    Q4_K_M

Hardware Requirements

Quantization	Recommended Memory
Q4_K_M	8–12 GB RAM

For the best inference performance, GPU acceleration is recommended.

License

This repository contains only GGUF conversions of the original model.

Please refer to the original model repository for the license and usage terms:

https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B

Credits

deepreinforce-ai for creating and releasing Ornith-1.0-9B.
ggml-org for developing llama.cpp and the GGUF tooling.
Hugging Face for model hosting and distribution.

If you find this GGUF conversion useful, please consider giving the repository a ❤️ on Hugging Face.

Downloads last month: 82

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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