Text Generation
Transformers
GGUF
English
jedi
cybersecurity
nanobot
swarm-intelligence
vitalis
lfm
liquid-foundation-model
lora
qlora
veritas
machiavelli
sovereign-ai
ferrell-synthetic-intelligence
conversational
Instructions to use FerrellSyntheticIntelligence/JEDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FerrellSyntheticIntelligence/JEDI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FerrellSyntheticIntelligence/JEDI") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FerrellSyntheticIntelligence/JEDI", dtype="auto") - llama-cpp-python
How to use FerrellSyntheticIntelligence/JEDI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FerrellSyntheticIntelligence/JEDI", filename="model/LFM2.5-1.2B-Instruct-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FerrellSyntheticIntelligence/JEDI 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 FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/JEDI: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 FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FerrellSyntheticIntelligence/JEDI: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 FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FerrellSyntheticIntelligence/JEDI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FerrellSyntheticIntelligence/JEDI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FerrellSyntheticIntelligence/JEDI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- SGLang
How to use FerrellSyntheticIntelligence/JEDI 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 "FerrellSyntheticIntelligence/JEDI" \ --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": "FerrellSyntheticIntelligence/JEDI", "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 "FerrellSyntheticIntelligence/JEDI" \ --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": "FerrellSyntheticIntelligence/JEDI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use FerrellSyntheticIntelligence/JEDI with Ollama:
ollama run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- Unsloth Studio
How to use FerrellSyntheticIntelligence/JEDI 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 FerrellSyntheticIntelligence/JEDI 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 FerrellSyntheticIntelligence/JEDI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FerrellSyntheticIntelligence/JEDI to start chatting
- Pi
How to use FerrellSyntheticIntelligence/JEDI with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI: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": "FerrellSyntheticIntelligence/JEDI:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FerrellSyntheticIntelligence/JEDI with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI: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 FerrellSyntheticIntelligence/JEDI:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use FerrellSyntheticIntelligence/JEDI with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI: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 "FerrellSyntheticIntelligence/JEDI: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 FerrellSyntheticIntelligence/JEDI with Docker Model Runner:
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- Lemonade
How to use FerrellSyntheticIntelligence/JEDI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FerrellSyntheticIntelligence/JEDI:Q4_K_M
Run and chat with the model
lemonade run user.JEDI-Q4_K_M
List all available models
lemonade list
| """ | |
| JEDI Mission Control — Human operator interface. | |
| The Mission Control is the operator's dashboard for: | |
| - Creating and managing missions | |
| - Deploying and monitoring nanobots | |
| - Reviewing intelligence | |
| - Making authorization decisions | |
| - Emergency shutdown | |
| """ | |
| import json | |
| import time | |
| from typing import Dict, List, Optional | |
| from ..core.engine import JEDIEngine, EngineState | |
| from ..core.mission import Mission, MissionStatus | |
| class MissionControl: | |
| def __init__(self, engine: Optional[JEDIEngine] = None): | |
| self.engine = engine or JEDIEngine() | |
| self.operation_log = [] | |
| self.alerts = [] | |
| def new_mission(self, name: str, mission_type: str, target: Dict, objectives: List[str], | |
| operator_id: str, jurisdiction: str, auth_ref: str) -> Dict: | |
| """Create a new mission through Mission Control.""" | |
| # Build authorization | |
| from ..legal.gate import LegalGate, AuthorizationLevel | |
| gate = LegalGate() | |
| # Map mission types to authorization levels | |
| level_map = { | |
| "recon": AuthorizationLevel.RECON, | |
| "defense": AuthorizationLevel.DEFEND, | |
| "pentest": AuthorizationLevel.OFFEND, | |
| "offense": AuthorizationLevel.OFFEND, | |
| "attribution": AuthorizationLevel.ATTRIBUTION, | |
| "sweep": AuthorizationLevel.RECON, | |
| "forensics": AuthorizationLevel.OBSERVE, | |
| } | |
| auth = gate.create_authorization( | |
| operator_id=operator_id, | |
| level=level_map.get(mission_type, AuthorizationLevel.RECON), | |
| jurisdiction=jurisdiction, | |
| written_auth_ref=auth_ref, | |
| mission_type=mission_type, | |
| target=target, | |
| ) | |
| # Build mission config | |
| config = { | |
| "name": name, | |
| "type": mission_type, | |
| "target": target, | |
| "objectives": objectives, | |
| "authorization": auth, | |
| "roe": { | |
| "proportionality_acknowledged": True, | |
| "distinction_acknowledged": True, | |
| "necessity_acknowledged": True, | |
| } | |
| } | |
| mission = Mission(config) | |
| mission.authorize(auth) | |
| self._log(f"Mission '{name}' created: {mission.id}") | |
| return { | |
| "mission_id": mission.id, | |
| "name": name, | |
| "status": mission.status.value, | |
| "authorization": auth, | |
| "objectives": objectives, | |
| } | |
| def deploy_nanobot(self, mission_id: str, nanobot_type: str, target: Dict) -> Dict: | |
| """Deploy a nanobot to a mission target.""" | |
| result = self.engine.deploy_nanobot(nanobot_type, mission_id, target) | |
| self._log(f"Deployed {nanobot_type} nanobot to mission {mission_id}") | |
| return result | |
| def deploy_swarm(self, mission_id: str, swarm_config: Dict) -> Dict: | |
| """Deploy a coordinated swarm of nanobots.""" | |
| from ..swarm.coordinator import SwarmCoordinator | |
| swarm = SwarmCoordinator(f"swarm_{mission_id}") | |
| deployed = [] | |
| for bot_config in swarm_config.get("nanobots", []): | |
| result = self.deploy_nanobot( | |
| mission_id, | |
| bot_config.get("type", "scout"), | |
| bot_config.get("target", {}) | |
| ) | |
| if "error" not in result: | |
| swarm.add_member(result["bot_id"], bot_config.get("type", "scout")) | |
| deployed.append(result) | |
| return { | |
| "swarm_id": swarm.swarm_id, | |
| "deployed": deployed, | |
| "total": len(deployed), | |
| "status": "deployed" if deployed else "failed", | |
| } | |
| def get_sitrep(self) -> Dict: | |
| """Get a full situation report.""" | |
| return self.engine.get_situation_report() | |
| def emergency_shutdown(self, reason: str, operator_id: str) -> Dict: | |
| """Execute emergency shutdown.""" | |
| self.engine.emergency_shutdown(reason) | |
| self._log(f"EMERGENCY SHUTDOWN by {operator_id}: {reason}") | |
| return { | |
| "status": "shutdown", | |
| "reason": reason, | |
| "operator": operator_id, | |
| "timestamp": time.time(), | |
| } | |
| def get_audit_trail(self) -> List[Dict]: | |
| """Get the full audit trail from all JEDI systems.""" | |
| engine_ledger = self.engine.ledger.export() | |
| return engine_ledger | |
| def _log(self, message: str): | |
| self.operation_log.append({ | |
| "message": message, | |
| "timestamp": time.time(), | |
| }) | |
| def dashboard(self) -> Dict: | |
| """Generate dashboard overview.""" | |
| sitrep = self.get_sitrep() | |
| return { | |
| "engine_state": sitrep["engine_state"], | |
| "threat_level": sitrep["threat_level"], | |
| "active_missions": sitrep["active_missions"], | |
| "deployed_nanobots": sitrep["deployed_nanobots"], | |
| "uptime_hours": round(sitrep["uptime_seconds"] / 3600, 2), | |
| "total_actions": sum( | |
| d.get("actions", 0) for d in sitrep.get("mission_details", {}).values() | |
| ), | |
| "alerts": self.alerts[-5:] if self.alerts else [], | |
| } | |