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 Comprehensive Benchmarks — Performance, throughput, stress tests. | |
| """ | |
| import sys, os, time, json, random, hashlib, statistics, threading | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) | |
| from jedi.core.engine import JEDIEngine, ThreatLevel | |
| from jedi.core.mission import Mission, MissionStatus | |
| from jedi.core.nanobot import Nanobot, NanobotType, NanobotState | |
| from jedi.legal.gate import LegalGate, AuthorizationLevel | |
| from jedi.swarm.coordinator import SwarmCoordinator, ConsensusProtocol, SharedWisdom | |
| from jedi.command.control import MissionControl | |
| from jedi.comms.channel import CommsChannel | |
| from jedi.memory.helix import HelixMemoryStore | |
| from jedi.nanobots.red.striker import StrikerNanobot | |
| from jedi.nanobots.blue.guardian import GuardianNanobot | |
| from jedi.nanobots.black.ghost import GhostNanobot | |
| from jedi.nanobots.purple.synapse import SynapseNanobot | |
| from jedi.nanobots.yellow.auditor import AuditorNanobot | |
| from jedi.nanobots.green.architect import ArchitectNanobot | |
| class Benchmark: | |
| def __init__(self): | |
| self.results = {} | |
| def time_it(self, name, func, iterations=100): | |
| times = [] | |
| for _ in range(iterations): | |
| start = time.perf_counter() | |
| result = func() | |
| end = time.perf_counter() | |
| times.append((end - start) * 1000) # ms | |
| self.results[name] = { | |
| "iterations": iterations, | |
| "min_ms": round(min(times), 3), | |
| "max_ms": round(max(times), 3), | |
| "avg_ms": round(statistics.mean(times), 3), | |
| "median_ms": round(statistics.median(times), 3), | |
| "p99_ms": round(sorted(times)[int(len(times) * 0.99)], 3), | |
| "total_ms": round(sum(times), 3), | |
| "ops_per_sec": round(1000 / statistics.mean(times), 1), | |
| } | |
| return self.results[name] | |
| def report(self): | |
| print(f"\n{'='*70}") | |
| print(f" JEDI BENCHMARK RESULTS") | |
| print(f"{'='*70}") | |
| print(f" {'Test':<45} {'ops/s':>8} {'avg ms':>8} {'p99 ms':>8}") | |
| print(f" {'-'*45} {'-'*8} {'-'*8} {'-'*8}") | |
| for name, data in self.results.items(): | |
| print(f" {name:<45} {data['ops_per_sec']:>8.1f} {data['avg_ms']:>8.3f} {data['p99_ms']:>8.3f}") | |
| print(f"{'='*70}\n") | |
| return self.results | |
| def bench_engine_init(b): | |
| def fn(): | |
| JEDIEngine() | |
| return b.time_it("Engine initialization", fn, 100) | |
| def bench_threat_assessment(b): | |
| engine = JEDIEngine() | |
| def fn(): | |
| engine.assess_threat({ | |
| "indicators": [{"type": "lateral_movement", "severity": 10}], | |
| "confidence": 0.5 | |
| }) | |
| return b.time_it("Threat assessment", fn, 500) | |
| def bench_situation_report(b): | |
| engine = JEDIEngine() | |
| def fn(): | |
| engine.get_situation_report() | |
| return b.time_it("Situation report", fn, 500) | |
| def bench_nanobot_create(b): | |
| def fn(): | |
| Nanobot(NanobotType.GHOST, "bench-mission", {"address": "10.0.0.1"}) | |
| return b.time_it("Nanobot creation", fn, 200) | |
| def bench_nanobot_heartbeat(b): | |
| bot = Nanobot(NanobotType.SENTINEL, "bench-mission", {"address": "10.0.0.1"}) | |
| def fn(): | |
| bot.heartbeat() | |
| return b.time_it("Nanobot heartbeat", fn, 1000) | |
| def bench_striker_exploit(b): | |
| s = StrikerNanobot("bench", {"address": "10.0.0.1"}) | |
| def fn(): | |
| s.exploit({"type": "sql_injection", "severity": 8}) | |
| return b.time_it("Striker exploit", fn, 200) | |
| def bench_guardian_detect(b): | |
| g = GuardianNanobot("bench", {"address": "10.0.0.1"}) | |
| baseline = {"network_traffic": 100, "failed_auth": 0, "processes": ["nginx"]} | |
| current = {"network_traffic": 500, "failed_auth": 20, "processes": ["nginx", "nc", "wget"]} | |
| def fn(): | |
| g.detect_anomaly(baseline, current) | |
| return b.time_it("Guardian anomaly detect", fn, 500) | |
| def bench_ghost_infiltrate(b): | |
| g = GhostNanobot("bench", {"address": "10.0.0.1"}) | |
| def fn(): | |
| g.infiltrate({"method": "supply_chain", "os": "linux"}) | |
| return b.time_it("Ghost infiltration", fn, 200) | |
| def bench_ghost_attribution(b): | |
| g = GhostNanobot("bench", {"address": "10.0.0.1"}) | |
| def fn(): | |
| g.collect_attribution({ | |
| "address": "10.0.0.1", "username": "root", "timezone": "US/Eastern", | |
| "installed_tools": ["cobalt_strike", "mimikatz"], "c2_beacons": ["cobalt_strike"] | |
| }) | |
| return b.time_it("Ghost attribution", fn, 200) | |
| def bench_auditor_compliance(b): | |
| a = AuditorNanobot("bench", {"address": "10.0.0.1"}) | |
| def fn(): | |
| a.check_compliance("nist_csf", {"logging_active": True, "mfa_enabled": True}) | |
| return b.time_it("Auditor compliance check", fn, 500) | |
| def bench_architect_review(b): | |
| a = ArchitectNanobot("bench", {"address": "10.0.0.1"}) | |
| code = "import os\nos.system(user_input)\neval(code)\nimport pickle\npickle.loads(data)" | |
| def fn(): | |
| a.review_code(code, "python") | |
| return b.time_it("Architect code review", fn, 300) | |
| def bench_synapse_coordinate(b): | |
| s = SynapseNanobot("bench", {"address": "10.0.0.1"}) | |
| def fn(): | |
| s.coordinate_attack_defense( | |
| {"steps": ["exploit", "escalate", "exfiltrate"]}, | |
| {"steps": ["detect", "contain", "recover"]} | |
| ) | |
| return b.time_it("Synapse coordination", fn, 300) | |
| def bench_legal_auth_create(b): | |
| gate = LegalGate() | |
| def fn(): | |
| gate.create_authorization( | |
| "OP-BENCH", AuthorizationLevel.OFFEND, "US-FEDERAL", | |
| "AUTH-BENCH", "pentest", {"address": "192.168.1.1"} | |
| ) | |
| return b.time_it("Legal auth creation", fn, 500) | |
| def bench_legal_verify(b): | |
| gate = LegalGate() | |
| auth = gate.create_authorization( | |
| "OP-BENCH", AuthorizationLevel.OFFEND, "US-FEDERAL", | |
| "AUTH-BENCH", "pentest", {"address": "192.168.1.1"} | |
| ) | |
| mission_config = { | |
| "type": "pentest", "target": {"address": "192.168.1.1"}, | |
| "authorization": auth, | |
| "roe": {"proportionality_acknowledged": True, "distinction_acknowledged": True, "necessity_acknowledged": True} | |
| } | |
| def fn(): | |
| gate.verify_authorization(mission_config) | |
| return b.time_it("Legal verification", fn, 1000) | |
| def bench_ledger_log(b): | |
| engine = JEDIEngine() | |
| def fn(): | |
| engine.ledger.log("bench_event", {"data": "test"}) | |
| return b.time_it("Ledger log entry", fn, 2000) | |
| def bench_ledger_verify(b): | |
| engine = JEDIEngine() | |
| for i in range(100): | |
| engine.ledger.log(f"event_{i}", {"data": i}) | |
| def fn(): | |
| engine.ledger.verify_integrity() | |
| return b.time_it("Ledger verify (100 entries)", fn, 200) | |
| def bench_swarm_add(b): | |
| swarm = SwarmCoordinator("bench-swarm") | |
| counter = [0] | |
| def fn(): | |
| counter[0] += 1 | |
| swarm.add_member(f"BENCH-{counter[0]:06d}", "scout") | |
| return b.time_it("Swarm member add", fn, 500) | |
| def bench_swarm_task(b): | |
| swarm = SwarmCoordinator("bench-swarm") | |
| for i in range(10): | |
| swarm.add_member(f"B-{i}", ["scout","guardian","striker"][i%3]) | |
| def fn(): | |
| swarm.queue_task({"type": "recon", "target": "10.0.0.1"}) | |
| return b.time_it("Swarm task queue", fn, 500) | |
| def bench_consensus(b): | |
| cp = ConsensusProtocol(threshold=0.6) | |
| proposals = [ | |
| {"bot_id": f"B-{i}", "action": random.choice(["recon", "defend", "attack"]), | |
| "confidence": random.uniform(0.5, 1.0)} | |
| for i in range(5) | |
| ] | |
| def fn(): | |
| cp.reach_consensus(proposals, min_voters=3) | |
| return b.time_it("Consensus protocol", fn, 1000) | |
| def bench_comms_c2(b): | |
| ch = CommsChannel("bench") | |
| def fn(): | |
| ch.send_c2("GHOST-001", {"intel": "test_data"}) | |
| return b.time_it("Comms C2 send", fn, 1000) | |
| def bench_comms_p2p(b): | |
| ch = CommsChannel("bench") | |
| def fn(): | |
| ch.send_p2p("BOT-001", "BOT-002", {"data": "test"}) | |
| return b.time_it("Comms P2P send", fn, 1000) | |
| def bench_memory_store(b): | |
| mem = HelixMemoryStore("/tmp/jedi_bench_memory") | |
| counter = [0] | |
| def fn(): | |
| counter[0] += 1 | |
| mem.store(f"key_{counter[0]}", {"data": f"value_{counter[0]}"}) | |
| return b.time_it("Memory store", fn, 1000) | |
| def bench_memory_retrieve(b): | |
| mem = HelixMemoryStore("/tmp/jedi_bench_memory") | |
| for i in range(1000): | |
| mem.store(f"key_{i}", {"data": i}) | |
| def fn(): | |
| mem.retrieve(f"key_{random.randint(0, 999)}") | |
| return b.time_it("Memory retrieve", fn, 2000) | |
| def bench_memory_search(b): | |
| mem = HelixMemoryStore("/tmp/jedi_bench_memory") | |
| for i in range(100): | |
| mem.store(f"threat_{i}", {"description": f"threat pattern {i}"}, "semantic") | |
| def fn(): | |
| mem.search("threat") | |
| return b.time_it("Memory search (100 entries)", fn, 200) | |
| def bench_mission_create(b): | |
| mc = MissionControl() | |
| def fn(): | |
| mc.new_mission( | |
| f"Bench-{random.randint(0,9999)}", "recon", | |
| {"address": "10.0.0.1"}, ["test"], | |
| "OP-BENCH", "US-FEDERAL", "AUTH-BENCH" | |
| ) | |
| return b.time_it("Mission creation", fn, 100) | |
| def bench_dashboard(b): | |
| mc = MissionControl() | |
| def fn(): | |
| mc.dashboard() | |
| return b.time_it("Dashboard render", fn, 500) | |
| def bench_swarm_intel_collect(b): | |
| swarm = SwarmCoordinator("bench") | |
| for i in range(20): | |
| swarm.add_member(f"B-{i}", "scout") | |
| def fn(): | |
| swarm.collect_intel(f"B-{random.randint(0,19)}", {"type": "scan", "result": "open_port"}) | |
| return b.time_it("Swarm intel collection", fn, 1000) | |
| def bench_pheromone(b): | |
| sw = SharedWisdom() | |
| counter = [0] | |
| def fn(): | |
| counter[0] += 1 | |
| sw.add_pheromone(f"trail_{counter[0] % 10}", 1.0) | |
| return b.time_it("Pheromone trail", fn, 2000) | |
| def bench_batch_nanobot_lifecycle(b): | |
| """Full lifecycle: create -> heartbeat -> recon -> report -> self-destruct""" | |
| def fn(): | |
| bot = Nanobot(NanobotType.SENTINEL, "bench", {"address": "10.0.0.1"}) | |
| bot.heartbeat() | |
| bot.recon({"open_ports": [80, 443], "services": ["nginx"]}) | |
| bot.report() | |
| bot.self_destruct() | |
| return b.time_it("Full nanobot lifecycle", fn, 200) | |
| def bench_concurrent_operations(b): | |
| """Test thread safety under concurrent load""" | |
| engine = JEDIEngine() | |
| results = [] | |
| def worker(): | |
| for _ in range(10): | |
| engine.assess_threat({"indicators": [{"type": "suspicious_process", "severity": 5}], "confidence": 0.5}) | |
| engine.ledger.log("concurrent_event", {"thread": threading.current_thread().name}) | |
| def fn(): | |
| threads = [threading.Thread(target=worker) for _ in range(10)] | |
| for t in threads: t.start() | |
| for t in threads: t.join() | |
| return b.time_it("Concurrent ops (10 threads)", fn, 50) | |
| if __name__ == "__main__": | |
| b = Benchmark() | |
| print("Running JEDI benchmarks...") | |
| print("(This may take a minute)") | |
| print() | |
| # Core system | |
| bench_engine_init(b) | |
| bench_threat_assessment(b) | |
| bench_situation_report(b) | |
| # Nanobot ops | |
| bench_nanobot_create(b) | |
| bench_nanobot_heartbeat(b) | |
| bench_batch_nanobot_lifecycle(b) | |
| # Color team ops | |
| bench_striker_exploit(b) | |
| bench_guardian_detect(b) | |
| bench_ghost_infiltrate(b) | |
| bench_ghost_attribution(b) | |
| bench_auditor_compliance(b) | |
| bench_architect_review(b) | |
| bench_synapse_coordinate(b) | |
| # Legal | |
| bench_legal_auth_create(b) | |
| bench_legal_verify(b) | |
| # Ledger | |
| bench_ledger_log(b) | |
| bench_ledger_verify(b) | |
| # Swarm | |
| bench_swarm_add(b) | |
| bench_swarm_task(b) | |
| bench_consensus(b) | |
| bench_swarm_intel_collect(b) | |
| bench_pheromone(b) | |
| # Comms | |
| bench_comms_c2(b) | |
| bench_comms_p2p(b) | |
| # Memory | |
| bench_memory_store(b) | |
| bench_memory_retrieve(b) | |
| bench_memory_search(b) | |
| # Mission Control | |
| bench_mission_create(b) | |
| bench_dashboard(b) | |
| # Stress | |
| bench_concurrent_operations(b) | |
| results = b.report() | |
| # Save results | |
| with open("benchmark_results.json", "w") as f: | |
| json.dump(results, f, indent=2) | |
| print("Results saved to benchmark_results.json") | |
| # Summary | |
| total_ops = sum(r["ops_per_sec"] for r in results.values()) | |
| print(f"\nTotal aggregate throughput: {total_ops:,.0f} operations/sec") | |
| print(f"Fastest: {min(results.values(), key=lambda x: x['avg_ms'])['avg_ms']:.3f} ms") | |
| print(f"Slowest: {max(results.values(), key=lambda x: x['avg_ms'])['avg_ms']:.3f} ms") | |