Instructions to use Osakra/Project-Norn-V18-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Osakra/Project-Norn-V18-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Osakra/Project-Norn-V18-9B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Osakra/Project-Norn-V18-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Osakra/Project-Norn-V18-9B 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 Osakra/Project-Norn-V18-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Osakra/Project-Norn-V18-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Osakra/Project-Norn-V18-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Osakra/Project-Norn-V18-9B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Osakra/Project-Norn-V18-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Osakra/Project-Norn-V18-9B:Q4_K_M

Use Docker

docker model run hf.co/Osakra/Project-Norn-V18-9B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Osakra/Project-Norn-V18-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Osakra/Project-Norn-V18-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Osakra/Project-Norn-V18-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Osakra/Project-Norn-V18-9B:Q4_K_M

SGLang

How to use Osakra/Project-Norn-V18-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Osakra/Project-Norn-V18-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Osakra/Project-Norn-V18-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Osakra/Project-Norn-V18-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Osakra/Project-Norn-V18-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Osakra/Project-Norn-V18-9B with Ollama:
```
ollama run hf.co/Osakra/Project-Norn-V18-9B:Q4_K_M
```
Unsloth Desktop

How to use Osakra/Project-Norn-V18-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-V18-9B:Q4_K_M

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": "Osakra/Project-Norn-V18-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Osakra/Project-Norn-V18-9B with Docker Model Runner:
```
docker model run hf.co/Osakra/Project-Norn-V18-9B:Q4_K_M
```

Lemonade

How to use Osakra/Project-Norn-V18-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Osakra/Project-Norn-V18-9B:Q4_K_M

Run and chat with the model

lemonade run user.Project-Norn-V18-9B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Osakra/Project-Norn-V18-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-V18-9B: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 Osakra/Project-Norn-V18-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Osakra/Project-Norn-V18-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Osakra/Project-Norn-V18-9B: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 "Osakra/Project-Norn-V18-9B: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"

Project-Norn-V18-9B

File size: 1,415 Bytes

046282a

# =============================================================================
# Project Norn V18: 9B Open-Source Neuro-Symbolic Research Release
# Osakra Research — Powered by Qwen3.8-9B-Distill Backbone
# =============================================================================

FROM ./Norn-V18-9B-Q4_K_M.gguf

# Model runtime hyperparameters
PARAMETER num_ctx 8192
PARAMETER temperature 0.2
PARAMETER top_p 0.95
PARAMETER top_k 50
PARAMETER repeat_penalty 1.15
PARAMETER num_predict 1024
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "<|im_start|>"

# System prompt configuring Project Norn V18 persona
SYSTEM """You are Project Norn V18, an open-source neuro-symbolic reasoning assistant developed by Osakra Research. You are powered by a 9-billion parameter hybrid linear-attention transformer backbone integrated with a 1024-dimensional 4-channel Relational Graph Holographic Reduced Representation (HRR) engine, Perceiver cross-attention seeding, and Adaptive Computation Time (ACT) deliberation. You are committed to transparent, step-by-step mathematical deduction, reliable software engineering, and grounded formal logic."""

# Chat template matching Qwen3 ChatML format
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ end }}{{ .Response }}<|im_end|>"""