Instructions to use AtomicChat/Ornith-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 AtomicChat/Ornith-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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use AtomicChat/Ornith-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Ornith-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtomicChat/Ornith-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Ollama
How to use AtomicChat/Ornith-9B-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use AtomicChat/Ornith-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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/Ornith-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Ornith-9B-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/Ornith-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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/Ornith-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 AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL

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 "AtomicChat/Ornith-9B-GGUF:UD-Q4_K_XL" \
  --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"

AlexAtomic commited on Jun 25

Commit

3a763fa

verified ·

1 Parent(s): ca79e7d

Atomic Chat self-quantized deepreinforce-ai/Ornith-1.0-9B ladder (imatrix, calibration_datav3)

Browse files

Files changed (8) hide show

.gitattributes +6 -0
README.md +6 -6
ornith-9b-IQ4_XS.gguf +3 -0
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@@ -75,12 +75,12 @@ Scores are DeepReinforce's published results for the base `deepreinforce-ai/Orni
 | Quant | Size | Notes |
 |---|---|---|
-| `IQ4_XS` | — | Excellent quality for size. Recommended low-bit. |
-| **`Q4_K_M`** | — | **Recommended default. Best balance of size, speed and quality.** |
-| **`UD-Q4_K_XL`** | — | **Dynamic. Token embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.** |
-| `Q5_K_M` | — | Higher quality, low loss. |
-| `Q6_K` | — | Near lossless. |
-| `Q8_0` | — | Effectively lossless, reference quality. |
 > [!TIP]
 > Pick the largest file that fits your (V)RAM with room for context. `Q4_K_M` or `UD-Q4_K_XL` is the sweet spot for most setups; `Q6_K` or `Q8_0` for maximum fidelity.

 | Quant | Size | Notes |
 |---|---|---|
+| `IQ4_XS` | 5.2 GB | Excellent quality for size. Recommended low-bit. |
+| **`Q4_K_M`** | 5.6 GB | **Recommended default. Best balance of size, speed and quality.** |
+| **`UD-Q4_K_XL`** | 6.4 GB | **Dynamic. Token embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.** |
+| `Q5_K_M` | 6.5 GB | Higher quality, low loss. |
+| `Q6_K` | 7.4 GB | Near lossless. |
+| `Q8_0` | 9.5 GB | Effectively lossless, reference quality. |
 > [!TIP]
 > Pick the largest file that fits your (V)RAM with room for context. `Q4_K_M` or `UD-Q4_K_XL` is the sweet spot for most setups; `Q6_K` or `Q8_0` for maximum fidelity.

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