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
Upload jedi/core/engine.py with huggingface_hub
Browse files- jedi/core/engine.py +326 -0
jedi/core/engine.py
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| 1 |
+
"""
|
| 2 |
+
JEDI Core Engine — The Brain of the Swarm
|
| 3 |
+
|
| 4 |
+
Integrates:
|
| 5 |
+
- Strategic planning (LLM-based)
|
| 6 |
+
- Swarm coordination (multi-agent RL)
|
| 7 |
+
- Threat analysis (pattern recognition)
|
| 8 |
+
- Legal gate (authorization enforcement)
|
| 9 |
+
|
| 10 |
+
Inspired by: Vitalis Cognitive Substrate + FSI_FELON Chimera
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
import json
|
| 14 |
+
import time
|
| 15 |
+
import threading
|
| 16 |
+
import hashlib
|
| 17 |
+
from enum import Enum
|
| 18 |
+
from typing import Optional, Dict, List, Any
|
| 19 |
+
from datetime import datetime
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
class EngineState(Enum):
|
| 23 |
+
IDLE = "idle"
|
| 24 |
+
PLANNING = "planning"
|
| 25 |
+
DEPLOYING = "deploying"
|
| 26 |
+
ACTIVE = "active"
|
| 27 |
+
PAUSED = "paused"
|
| 28 |
+
EMERGENCY = "emergency"
|
| 29 |
+
SHUTDOWN = "shutdown"
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
class ThreatLevel(Enum):
|
| 33 |
+
NONE = 0
|
| 34 |
+
LOW = 1
|
| 35 |
+
MEDIUM = 2
|
| 36 |
+
HIGH = 3
|
| 37 |
+
CRITICAL = 4
|
| 38 |
+
NATION_STATE = 5
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
class JEDIEngine:
|
| 42 |
+
"""
|
| 43 |
+
The JEDI Core AI Engine coordinates all nanobot operations.
|
| 44 |
+
|
| 45 |
+
Architecture:
|
| 46 |
+
- Strategic Planner: Mission decomposition and resource allocation
|
| 47 |
+
- Swarm Coordinator: Multi-agent orchestration and consensus
|
| 48 |
+
- Threat Analyzer: Pattern recognition and adversary profiling
|
| 49 |
+
- Legal Gate: Authorization verification and rules of engagement
|
| 50 |
+
"""
|
| 51 |
+
|
| 52 |
+
def __init__(self, config: Optional[Dict] = None):
|
| 53 |
+
self.config = config or self._default_config()
|
| 54 |
+
self.state = EngineState.IDLE
|
| 55 |
+
self.threat_level = ThreatLevel.NONE
|
| 56 |
+
self.ledger = Ledger()
|
| 57 |
+
self.active_missions = {}
|
| 58 |
+
self.deployed_nanobots = {}
|
| 59 |
+
self.swarm_memory = SwarmMemory()
|
| 60 |
+
self._lock = threading.Lock()
|
| 61 |
+
self._start_time = time.time()
|
| 62 |
+
|
| 63 |
+
self.ledger.log("engine_init", {
|
| 64 |
+
"version": "0.1.0",
|
| 65 |
+
"config": self.config,
|
| 66 |
+
"timestamp": datetime.utcnow().isoformat()
|
| 67 |
+
})
|
| 68 |
+
|
| 69 |
+
def _default_config(self) -> Dict:
|
| 70 |
+
return {
|
| 71 |
+
"max_nanobots": 1000,
|
| 72 |
+
"max_concurrent_missions": 10,
|
| 73 |
+
"heartbeat_interval": 30,
|
| 74 |
+
"self_destruct_timeout": 3600,
|
| 75 |
+
"legal_gate_required": True,
|
| 76 |
+
"human_in_loop": True,
|
| 77 |
+
"swarm_consensus_threshold": 0.7,
|
| 78 |
+
"threat_auto_escalate": True,
|
| 79 |
+
"encryption": "post_quantum",
|
| 80 |
+
"audit_all_actions": True,
|
| 81 |
+
}
|
| 82 |
+
|
| 83 |
+
def assess_threat(self, intel: Dict) -> ThreatLevel:
|
| 84 |
+
"""Analyze incoming intelligence and assess threat level."""
|
| 85 |
+
indicators = intel.get("indicators", [])
|
| 86 |
+
confidence = intel.get("confidence", 0.0)
|
| 87 |
+
|
| 88 |
+
score = 0
|
| 89 |
+
for indicator in indicators:
|
| 90 |
+
itype = indicator.get("type", "")
|
| 91 |
+
severity = indicator.get("severity", 0)
|
| 92 |
+
|
| 93 |
+
if itype == "nation_state_attribution":
|
| 94 |
+
score += 50
|
| 95 |
+
elif itype == "apt_group":
|
| 96 |
+
score += 35
|
| 97 |
+
elif itype == "ransomware":
|
| 98 |
+
score += 30
|
| 99 |
+
elif itype == "data_exfiltration":
|
| 100 |
+
score += 25
|
| 101 |
+
elif itype == "lateral_movement":
|
| 102 |
+
score += 15
|
| 103 |
+
elif itype == "suspicious_process":
|
| 104 |
+
score += 10
|
| 105 |
+
elif itype == "anomalous_traffic":
|
| 106 |
+
score += 5
|
| 107 |
+
|
| 108 |
+
score += severity
|
| 109 |
+
|
| 110 |
+
score *= confidence
|
| 111 |
+
|
| 112 |
+
if score >= 80:
|
| 113 |
+
self.threat_level = ThreatLevel.NATION_STATE
|
| 114 |
+
elif score >= 60:
|
| 115 |
+
self.threat_level = ThreatLevel.CRITICAL
|
| 116 |
+
elif score >= 40:
|
| 117 |
+
self.threat_level = ThreatLevel.HIGH
|
| 118 |
+
elif score >= 20:
|
| 119 |
+
self.threat_level = ThreatLevel.MEDIUM
|
| 120 |
+
elif score >= 5:
|
| 121 |
+
self.threat_level = ThreatLevel.LOW
|
| 122 |
+
else:
|
| 123 |
+
self.threat_level = ThreatLevel.NONE
|
| 124 |
+
|
| 125 |
+
self.ledger.log("threat_assessment", {
|
| 126 |
+
"score": score,
|
| 127 |
+
"level": self.threat_level.name,
|
| 128 |
+
"indicators_count": len(indicators)
|
| 129 |
+
})
|
| 130 |
+
|
| 131 |
+
return self.threat_level
|
| 132 |
+
|
| 133 |
+
def create_mission(self, mission_config: Dict) -> Dict:
|
| 134 |
+
"""Create a new mission with legal gate verification."""
|
| 135 |
+
if self.config["legal_gate_required"]:
|
| 136 |
+
from ..legal.gate import LegalGate
|
| 137 |
+
gate = LegalGate()
|
| 138 |
+
auth_result = gate.verify_authorization(mission_config)
|
| 139 |
+
if not auth_result["authorized"]:
|
| 140 |
+
return {
|
| 141 |
+
"error": "Authorization denied",
|
| 142 |
+
"reason": auth_result["reason"],
|
| 143 |
+
"required": auth_result["required_level"]
|
| 144 |
+
}
|
| 145 |
+
|
| 146 |
+
mission_id = hashlib.sha256(
|
| 147 |
+
f"{time.time()}_{json.dumps(mission_config)}".encode()
|
| 148 |
+
).hexdigest()[:16]
|
| 149 |
+
|
| 150 |
+
mission = {
|
| 151 |
+
"id": mission_id,
|
| 152 |
+
"config": mission_config,
|
| 153 |
+
"status": "created",
|
| 154 |
+
"created_at": datetime.utcnow().isoformat(),
|
| 155 |
+
"nanobots_deployed": [],
|
| 156 |
+
"intel_collected": [],
|
| 157 |
+
"actions_taken": [],
|
| 158 |
+
"authorization": auth_result if self.config["legal_gate_required"] else {"authorized": True},
|
| 159 |
+
}
|
| 160 |
+
|
| 161 |
+
with self._lock:
|
| 162 |
+
self.active_missions[mission_id] = mission
|
| 163 |
+
|
| 164 |
+
self.ledger.log("mission_created", {
|
| 165 |
+
"mission_id": mission_id,
|
| 166 |
+
"type": mission_config.get("type", "unknown"),
|
| 167 |
+
"target": mission_config.get("target", "unknown")
|
| 168 |
+
})
|
| 169 |
+
|
| 170 |
+
return mission
|
| 171 |
+
|
| 172 |
+
def deploy_nanobot(self, nanobot_type: str, mission_id: str, target: Dict) -> Dict:
|
| 173 |
+
"""Deploy a nanobot to a target."""
|
| 174 |
+
from .nanobot import Nanobot, NanobotType
|
| 175 |
+
|
| 176 |
+
if mission_id not in self.active_missions:
|
| 177 |
+
return {"error": "Mission not found"}
|
| 178 |
+
|
| 179 |
+
mission = self.active_missions[mission_id]
|
| 180 |
+
if mission["status"] == "paused":
|
| 181 |
+
return {"error": "Mission is paused"}
|
| 182 |
+
|
| 183 |
+
bot = Nanobot(
|
| 184 |
+
nanobot_type=NanobotType(nanobot_type),
|
| 185 |
+
mission_id=mission_id,
|
| 186 |
+
target=target
|
| 187 |
+
)
|
| 188 |
+
|
| 189 |
+
with self._lock:
|
| 190 |
+
self.deployed_nanobots[bot.id] = bot
|
| 191 |
+
mission["nanobots_deployed"].append(bot.id)
|
| 192 |
+
|
| 193 |
+
self.ledger.log("nanobot_deployed", {
|
| 194 |
+
"bot_id": bot.id,
|
| 195 |
+
"type": nanobot_type,
|
| 196 |
+
"mission_id": mission_id,
|
| 197 |
+
"target": target.get("address", "unknown")
|
| 198 |
+
})
|
| 199 |
+
|
| 200 |
+
return {
|
| 201 |
+
"bot_id": bot.id,
|
| 202 |
+
"type": nanobot_type,
|
| 203 |
+
"status": "deployed",
|
| 204 |
+
"mission_id": mission_id
|
| 205 |
+
}
|
| 206 |
+
|
| 207 |
+
def get_situation_report(self) -> Dict:
|
| 208 |
+
"""Generate a comprehensive situation report."""
|
| 209 |
+
uptime = time.time() - self._start_time
|
| 210 |
+
return {
|
| 211 |
+
"engine_state": self.state.value,
|
| 212 |
+
"threat_level": self.threat_level.name,
|
| 213 |
+
"uptime_seconds": round(uptime, 1),
|
| 214 |
+
"active_missions": len(self.active_missions),
|
| 215 |
+
"deployed_nanobots": len(self.deployed_nanobots),
|
| 216 |
+
"mission_details": {
|
| 217 |
+
mid: {
|
| 218 |
+
"status": m["status"],
|
| 219 |
+
"nanobots": len(m["nanobots_deployed"]),
|
| 220 |
+
"intel_count": len(m["intel_collected"]),
|
| 221 |
+
"actions": len(m["actions_taken"])
|
| 222 |
+
}
|
| 223 |
+
for mid, m in self.active_missions.items()
|
| 224 |
+
},
|
| 225 |
+
"nanobot_status": {
|
| 226 |
+
bid: {
|
| 227 |
+
"type": b.nanobot_type.value,
|
| 228 |
+
"state": b.state,
|
| 229 |
+
"uptime": round(time.time() - b.deploy_time, 1)
|
| 230 |
+
}
|
| 231 |
+
for bid, b in self.deployed_nanobots.items()
|
| 232 |
+
},
|
| 233 |
+
"ledger_entries": len(self.ledger.entries),
|
| 234 |
+
"config": self.config
|
| 235 |
+
}
|
| 236 |
+
|
| 237 |
+
def emergency_shutdown(self, reason: str):
|
| 238 |
+
"""Emergency shutdown of all operations."""
|
| 239 |
+
self.state = EngineState.SHUTDOWN
|
| 240 |
+
|
| 241 |
+
with self._lock:
|
| 242 |
+
for bot_id, bot in self.deployed_nanobots.items():
|
| 243 |
+
bot.self_destruct()
|
| 244 |
+
for mission_id, mission in self.active_missions.items():
|
| 245 |
+
mission["status"] = "emergency_shutdown"
|
| 246 |
+
|
| 247 |
+
self.ledger.log("emergency_shutdown", {
|
| 248 |
+
"reason": reason,
|
| 249 |
+
"nanobots_terminated": len(self.deployed_nanobots),
|
| 250 |
+
"missions_aborted": len(self.active_missions)
|
| 251 |
+
})
|
| 252 |
+
|
| 253 |
+
|
| 254 |
+
class Ledger:
|
| 255 |
+
"""
|
| 256 |
+
Cryptographic Conscience — Immutable audit trail.
|
| 257 |
+
Inspired by Vitalis Core's Ledger system.
|
| 258 |
+
Every action is SHA-256 hashed and chained.
|
| 259 |
+
"""
|
| 260 |
+
|
| 261 |
+
def __init__(self):
|
| 262 |
+
self.entries = []
|
| 263 |
+
self._previous_hash = "0" * 64
|
| 264 |
+
|
| 265 |
+
def log(self, event_type: str, data: Dict):
|
| 266 |
+
entry = {
|
| 267 |
+
"timestamp": datetime.utcnow().isoformat(),
|
| 268 |
+
"event_type": event_type,
|
| 269 |
+
"data": data,
|
| 270 |
+
"previous_hash": self._previous_hash,
|
| 271 |
+
}
|
| 272 |
+
|
| 273 |
+
entry_str = json.dumps(entry, sort_keys=True)
|
| 274 |
+
entry["hash"] = hashlib.sha256(entry_str.encode()).hexdigest()
|
| 275 |
+
self._previous_hash = entry["hash"]
|
| 276 |
+
self.entries.append(entry)
|
| 277 |
+
|
| 278 |
+
def verify_integrity(self) -> bool:
|
| 279 |
+
"""Verify the entire ledger chain is unbroken."""
|
| 280 |
+
previous = "0" * 64
|
| 281 |
+
for entry in self.entries:
|
| 282 |
+
if entry["previous_hash"] != previous:
|
| 283 |
+
return False
|
| 284 |
+
check = {k: v for k, v in entry.items() if k != "hash"}
|
| 285 |
+
check_str = json.dumps(check, sort_keys=True)
|
| 286 |
+
if hashlib.sha256(check_str.encode()).hexdigest() != entry["hash"]:
|
| 287 |
+
return False
|
| 288 |
+
previous = entry["hash"]
|
| 289 |
+
return True
|
| 290 |
+
|
| 291 |
+
def export(self) -> List[Dict]:
|
| 292 |
+
return list(self.entries)
|
| 293 |
+
|
| 294 |
+
|
| 295 |
+
class SwarmMemory:
|
| 296 |
+
"""
|
| 297 |
+
Shared memory across all nanobots in the swarm.
|
| 298 |
+
Uses consensus-based writes to prevent corruption.
|
| 299 |
+
"""
|
| 300 |
+
|
| 301 |
+
def __init__(self):
|
| 302 |
+
self.store = {}
|
| 303 |
+
self._lock = threading.Lock()
|
| 304 |
+
self._write_votes = {}
|
| 305 |
+
|
| 306 |
+
def read(self, key: str) -> Optional[Any]:
|
| 307 |
+
with self._lock:
|
| 308 |
+
return self.store.get(key)
|
| 309 |
+
|
| 310 |
+
def write(self, key: str, value: Any, voter_id: str):
|
| 311 |
+
"""Consensus-based write — requires multiple nanobot votes."""
|
| 312 |
+
with self._lock:
|
| 313 |
+
if key not in self._write_votes:
|
| 314 |
+
self._write_votes[key] = {}
|
| 315 |
+
self._write_votes[key][voter_id] = value
|
| 316 |
+
|
| 317 |
+
if len(self._write_votes[key]) >= 3:
|
| 318 |
+
from collections import Counter
|
| 319 |
+
values = list(self._write_votes[key].values())
|
| 320 |
+
most_common = Counter([json.dumps(v, sort_keys=True) for v in values]).most_common(1)[0]
|
| 321 |
+
self.store[key] = json.loads(most_common[0])
|
| 322 |
+
del self._write_votes[key]
|
| 323 |
+
|
| 324 |
+
def snapshot(self) -> Dict:
|
| 325 |
+
with self._lock:
|
| 326 |
+
return dict(self.store)
|