Text Generation
Transformers
GGUF
English
continuous-latent-cot
neuro-symbolic
holographic-memory
relational-graph-hrr
reasoning
math
code
agentic
qwen3.8
osakra-research
conversational
Instructions to use Osakra/Project-Norn-V18-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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 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/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 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
- 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
- Pi
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"
File size: 2,556 Bytes
046282a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | """
Project Norn V18: Quickstart Inference Demonstration
Osakra Research — 9B Frontier Neuro-Symbolic Intelligence
Powered by Qwen3.8-9B-Distill Backbone
"""
import os
import sys
import json
import time
import urllib.request
OLLAMA_API = "http://localhost:11434/api/generate"
MODEL_NAME = "norn-v18"
def test_inference(query: str, title: str):
print("\n" + "=" * 70)
print(f" {title}")
print("=" * 70)
print(f"[Prompt]: {query}\n")
payload = {
"model": MODEL_NAME,
"prompt": query,
"stream": False,
"options": {
"num_ctx": 8192,
"temperature": 0.2,
"top_p": 0.95,
"repeat_penalty": 1.15
}
}
t0 = time.time()
req = urllib.request.Request(
OLLAMA_API,
data=json.dumps(payload).encode("utf-8"),
headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req) as res:
data = json.loads(res.read().decode("utf-8"))
dt = time.time() - t0
resp = data.get("response", "").strip()
print(f"[Norn V18 Response ({dt:.2f}s)]:\n{resp}\n")
def run_demos():
print("=" * 80)
print(" PROJECT NORN V18: INFERENCE DEMONSTRATION")
print("=" * 80)
# Demo 1: Formal Logic & Self-Awareness
test_inference(
"Introduce yourself, explain your 9B architecture, and describe your 1024-d Relational HRR memory.",
"DEMO 1: Self-Identity & Architecture Verification"
)
# Demo 2: Mathematical Deduction & Constraint Traps
test_inference(
"Tank A and Tank B start empty. Pump 1 fills Tank A at 4 L/min. Pump 2 fills Tank B at 6 L/min. "
"Tank A has a hole that leaks 1 L/min once it reaches 20 L. Tank B has no leak. At time t = 0, "
"both pumps are turned on. For what positive time t (in minutes, t > 0) is Tank B's volume exactly "
"twice Tank A's volume? Explain clearly.",
"DEMO 2: Mathematical Equation & Constraint Trap"
)
# Demo 3: Negative Constraints & Discipline
test_inference(
"List exactly 5 European capital cities. Rule 1: Do NOT include any city whose English name "
"contains the letters 'a' or 'e'. Rule 2: Provide the list as a numbered list with country name in parentheses. "
"Rule 3: Output ONLY the list with no preamble or intro.",
"DEMO 3: Strict Negative Constraint Following"
)
print("=" * 80)
print(" Demonstration completed successfully!")
print("=" * 80)
if __name__ == "__main__":
run_demos()
|