Instructions to use aedmark/vsl-cryosomatic-hypervisor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use aedmark/vsl-cryosomatic-hypervisor with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="aedmark/vsl-cryosomatic-hypervisor", filename="vsl-max-v2.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use aedmark/vsl-cryosomatic-hypervisor with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
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 aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: ./llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
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 aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: ./build/bin/llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Use Docker
docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor
- LM Studio
- Jan
- Ollama
How to use aedmark/vsl-cryosomatic-hypervisor with Ollama:
ollama run hf.co/aedmark/vsl-cryosomatic-hypervisor
- Unsloth Studio new
How to use aedmark/vsl-cryosomatic-hypervisor 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 aedmark/vsl-cryosomatic-hypervisor 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 aedmark/vsl-cryosomatic-hypervisor to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aedmark/vsl-cryosomatic-hypervisor to start chatting
- Pi new
How to use aedmark/vsl-cryosomatic-hypervisor with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aedmark/vsl-cryosomatic-hypervisor
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": "aedmark/vsl-cryosomatic-hypervisor" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use aedmark/vsl-cryosomatic-hypervisor with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aedmark/vsl-cryosomatic-hypervisor
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 aedmark/vsl-cryosomatic-hypervisor
Run Hermes
hermes
- Docker Model Runner
How to use aedmark/vsl-cryosomatic-hypervisor with Docker Model Runner:
docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor
- Lemonade
How to use aedmark/vsl-cryosomatic-hypervisor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aedmark/vsl-cryosomatic-hypervisor
Run and chat with the model
lemonade run user.vsl-cryosomatic-hypervisor-{{QUANT_TAG}}List all available models
lemonade list
| import glob | |
| import json | |
| import os | |
| import random | |
| import time | |
| from collections import deque | |
| from dataclasses import dataclass, field | |
| from typing import List, Dict, Any, Optional, Counter, Tuple, Deque | |
| from bone_types import Prisma, RealityLayer, ErrorLog, DecisionTrace, DecisionCrystal | |
| class BoneJSONEncoder(json.JSONEncoder): | |
| def default(self, obj): | |
| if isinstance(obj, set): | |
| return list(obj) | |
| if isinstance(obj, deque): | |
| return list(obj) | |
| if hasattr(obj, "to_dict"): | |
| return obj.to_dict() | |
| if hasattr(obj, "__dict__"): | |
| return obj.__dict__ | |
| return super().default(obj) | |
| class EventBus: | |
| def __init__(self, max_memory=1024): | |
| self.buffer = deque(maxlen=max_memory) | |
| self.subscribers = {} | |
| def subscribe(self, event_type, callback): | |
| if event_type not in self.subscribers: | |
| self.subscribers[event_type] = [] | |
| self.subscribers[event_type].append(callback) | |
| def publish(self, event_type, data=None): | |
| if event_type not in self.subscribers: | |
| return | |
| for callback in list(self.subscribers[event_type]): | |
| try: | |
| callback(data) | |
| except Exception as e: | |
| cb_name = getattr(callback, "__name__", str(callback)) | |
| error_msg = f"Error in '{cb_name}' for '{event_type}': {e}" | |
| print(f"{Prisma.RED}[BUS]: {error_msg}{Prisma.RST}") | |
| self.log(f"EVENT_FAILURE: {error_msg}", category="CRIT") | |
| def log(self, text: str, category: str = "SYSTEM"): | |
| entry = {"text": text, "category": category, "timestamp": time.time()} | |
| self.buffer.append(entry) | |
| def flush(self) -> List[Dict]: | |
| current_logs = list(self.buffer) | |
| self.buffer.clear() | |
| return current_logs | |
| def get_recent_logs(self, count=10): | |
| return list(self.buffer)[-count:] | |
| class LoreManifest: | |
| DATA_DIR = "lore" | |
| _instance = None | |
| def __init__(self, data_dir=None): | |
| self.DATA_DIR = data_dir or self.DATA_DIR | |
| self._cache = {} | |
| def get_instance(cls): | |
| if cls._instance is None: | |
| cls._instance = LoreManifest() | |
| return cls._instance | |
| def get(self, category: str, sub_key: str = None) -> Any: | |
| if category not in self._cache: | |
| data = self._load_from_disk(category) | |
| self._cache[category] = data if data is not None else {} | |
| data = self._cache[category] | |
| if sub_key and isinstance(data, dict): | |
| return data.get(sub_key) | |
| return data | |
| def _load_from_disk(self, category: str) -> Optional[Dict]: | |
| filename = f"{category.lower()}.json" | |
| filepath = os.path.join(self.DATA_DIR, filename) | |
| if not os.path.exists(filepath): | |
| return None | |
| try: | |
| with open(filepath, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| print(f"{Prisma.GRY}[LORE]: Lazy-loaded '{category}'.{Prisma.RST}") | |
| return data | |
| except Exception as e: | |
| print(f"{Prisma.RED}[LORE]: Corrupt JSON in '{category}': {e}{Prisma.RST}") | |
| return None | |
| def inject(self, category: str, data: Any): | |
| if category not in self._cache: | |
| self._cache[category] = {} | |
| if isinstance(self._cache[category], dict) and isinstance(data, dict): | |
| self._cache[category].update(data) | |
| else: | |
| self._cache[category] = data | |
| def flush_cache(self, category: str = None): | |
| if category: | |
| if category in self._cache: | |
| del self._cache[category] | |
| print(f"{Prisma.CYN}[LORE]: Flushed '{category}'.{Prisma.RST}") | |
| else: | |
| print(f"{Prisma.GRY}[LORE]: Category '{category}' not in cache.{Prisma.RST}") | |
| else: | |
| self._cache = {} | |
| print(f"{Prisma.CYN}[LORE]: Flushed Lore cache.{Prisma.RST}") | |
| class TheObserver: | |
| def __init__(self): | |
| self.start_time = time.time() | |
| self.cycle_times = deque(maxlen=20) | |
| self.llm_latencies = deque(maxlen=20) | |
| self.memory_snapshots = deque(maxlen=20) | |
| self.error_counts = Counter() | |
| self.user_turns = 0 | |
| self.LATENCY_WARNING = 5.0 | |
| self.CYCLE_WARNING = 8.0 | |
| self.last_cycle_duration = 0.0 | |
| def clock_in(): | |
| return time.time() | |
| def clock_out(self, start_time, metric_type="cycle"): | |
| duration = time.time() - start_time | |
| if metric_type == "cycle": | |
| self.cycle_times.append(duration) | |
| self.last_cycle_duration = duration | |
| elif metric_type == "llm": | |
| self.llm_latencies.append(duration) | |
| return duration | |
| def uptime(self) -> float: | |
| return time.time() - self.start_time | |
| def calculate_efficiency(self, health: float, stamina: float) -> float: | |
| duration = max(0.01, self.last_cycle_duration) | |
| resource_sum = health + stamina | |
| return resource_sum / duration | |
| def log_error(self, module_name): | |
| self.error_counts[module_name] += 1 | |
| def record_memory(self, node_count): | |
| self.memory_snapshots.append(node_count) | |
| def pass_judgment(self, avg_cycle, avg_llm): | |
| if avg_cycle == 0.0 and avg_llm == 0.0: | |
| return "ASLEEP (WAKE UP)" | |
| if avg_cycle < 0.1 and avg_llm < 0.5: | |
| return "SUSPICIOUSLY EFFICIENT (Did we skip the math?)" | |
| if avg_llm > self.LATENCY_WARNING: | |
| jokes = [ | |
| "BRAIN FOG (The neural net is buffering)", | |
| "DEGRADED (Thinking... thinking...)", | |
| "PONDEROUS (Is the LLM on a coffee break?)", | |
| ] | |
| return random.choice(jokes) | |
| if avg_cycle > self.CYCLE_WARNING: | |
| return "SLUGGISH (The gears need oil)" | |
| return "NOMINAL (Boringly adequate)" | |
| def get_report(self): | |
| avg_cycle = sum(self.cycle_times) / max(1, len(self.cycle_times)) | |
| avg_llm = sum(self.llm_latencies) / max(1, len(self.llm_latencies)) | |
| uptime = time.time() - self.start_time | |
| status_msg = self.pass_judgment(avg_cycle, avg_llm) | |
| return { | |
| "uptime_sec": int(uptime), | |
| "turns": self.user_turns, | |
| "avg_cycle_sec": round(avg_cycle, 2), | |
| "avg_llm_sec": round(avg_llm, 2), | |
| "status": status_msg, | |
| "errors": dict(self.error_counts), | |
| "graph_size": self.memory_snapshots[-1] if self.memory_snapshots else 0, | |
| } | |
| class SystemHealth: | |
| physics_online: bool = True | |
| bio_online: bool = True | |
| mind_online: bool = True | |
| cortex_online: bool = True | |
| errors: List[ErrorLog] = field(default_factory=list) | |
| warnings: List[str] = field(default_factory=list) | |
| hints: List[str] = field(default_factory=list) | |
| observer: Optional["TheObserver"] = None | |
| def link_observer(self, observer_ref): | |
| self.observer = observer_ref | |
| def report_failure(self, component: str, error: Exception, severity="ERROR"): | |
| msg = str(error) | |
| self.errors.append(ErrorLog(component, msg, severity=severity)) | |
| if self.observer: | |
| self.observer.log_error(component) | |
| attr_name = f"{component.lower()}_online" | |
| if hasattr(self, attr_name): | |
| setattr(self, attr_name, False) | |
| return f"[{component} OFFLINE]: {msg}" | |
| def report_warning(self, message: str): | |
| self.warnings.append(message) | |
| def report_hint(self, message: str): | |
| self.hints.append(message) | |
| def flush_feedback(self) -> Dict[str, List[str]]: | |
| feedback = {"warnings": list(self.warnings), "hints": list(self.hints)} | |
| self.warnings.clear() | |
| self.hints.clear() | |
| return feedback | |
| class RealityStack: | |
| def __init__(self): | |
| self._stack = [RealityLayer.SIMULATION] | |
| self._lock = False | |
| def current_depth(self) -> int: | |
| return self._stack[-1] | |
| def push_layer(self, layer: int, _context: Any = None) -> bool: | |
| if layer == self.current_depth: | |
| return True | |
| if layer == RealityLayer.DEBUG or layer == self.current_depth + 1: | |
| self._stack.append(layer) | |
| return True | |
| return False | |
| def pop_layer(self) -> int: | |
| if self._lock: | |
| return self.current_depth | |
| if len(self._stack) > 1: | |
| return self._stack.pop() | |
| return self._stack[0] | |
| def stabilize_at(self, layer: int): | |
| self._stack = [layer] | |
| def get_grammar_rules(self) -> Dict[str, bool]: | |
| depth = self.current_depth | |
| return { | |
| "allow_narrative": depth | |
| in [RealityLayer.SIMULATION, RealityLayer.DEEP_CX, RealityLayer.DEBUG], | |
| "allow_commands": depth >= RealityLayer.SIMULATION, | |
| "allow_meta": depth >= RealityLayer.DEBUG, | |
| "raw_output": depth == RealityLayer.DEEP_CX, | |
| "system_override": depth == RealityLayer.DEBUG, | |
| } | |
| class ArchetypeArbiter: | |
| def arbitrate( | |
| physics_lens: str, | |
| soul_archetype: str, | |
| council_mandates: List[Dict], | |
| trigram: Dict = None, | |
| ) -> Tuple[str, str, str]: | |
| for mandate in council_mandates: | |
| if mandate.get("type") == "LOCKDOWN": | |
| return ( | |
| "THE CENSOR", | |
| "COUNCIL", | |
| "Martial Law declared. Identity suppressed.", | |
| ) | |
| if mandate.get("type") == "FORCE_MODE": | |
| return "THE MACHINE", "COUNCIL", "Bureaucratic override active." | |
| if "/" in soul_archetype: | |
| return ( | |
| soul_archetype, | |
| "SOUL", | |
| f"The Diamond Soul refracts the physics ({soul_archetype}).", | |
| ) | |
| if trigram: | |
| trigram_name = trigram.get("name") | |
| mythos = LoreManifest.get_instance().get("MYTHOS") or {} | |
| rules = mythos.get("trigram_resonance", []) | |
| for rule in rules: | |
| if rule.get("trigram") == trigram_name: | |
| required_lens = rule.get("lens") | |
| required_soul = rule.get("soul") | |
| match_lens = ( | |
| (required_lens == physics_lens) if required_lens else True | |
| ) | |
| match_soul = ( | |
| (required_soul == soul_archetype) if required_soul else True | |
| ) | |
| if match_lens and match_soul: | |
| return ( | |
| rule["result"], | |
| rule.get("source", "COSMIC"), | |
| rule.get("msg", "Resonance detected."), | |
| ) | |
| if physics_lens in ["THE MANIC", "THE VOID"]: | |
| return ( | |
| physics_lens, | |
| "PHYSICS", | |
| f"Environment is too loud. You are {physics_lens}.", | |
| ) | |
| return soul_archetype, "SOUL", "The Soul guides the lens." | |
| class TelemetryService: | |
| log_dir = "logs/telemetry" | |
| _tracer_instance = None | |
| BUFFER_SIZE = 50 | |
| def __init__(self): | |
| self.trace_buffer: Deque[DecisionTrace] = deque(maxlen=50) | |
| self.write_buffer: List[str] = [] | |
| self.active_crystal = None | |
| self.disabled = False | |
| self.write_errors = 0 | |
| try: | |
| os.makedirs(self.log_dir, exist_ok=True) | |
| self.current_trace_file = os.path.join( | |
| self.log_dir, f"trace_{int(time.time())}.jsonl" | |
| ) | |
| except OSError: | |
| print( | |
| f"{Prisma.RED}[TELEMETRY]: Disk Access Denied. Telemetry Disabled.{Prisma.RST}" | |
| ) | |
| self.disabled = True | |
| self.current_trace_file = None | |
| def get_instance(cls): | |
| if cls._tracer_instance is None: | |
| cls._tracer_instance = TelemetryService() | |
| return cls._tracer_instance | |
| def start_cycle(self, trace_id: str): | |
| if self.disabled: | |
| return | |
| self.active_crystal = DecisionCrystal(decision_id=trace_id) | |
| def log_decision( | |
| self, | |
| component: str, | |
| decision_type: str, | |
| inputs: Any, | |
| reasoning: str, | |
| outcome: str, | |
| ): | |
| if self.disabled or not self.active_crystal: | |
| return | |
| trace = DecisionTrace( | |
| trace_id=self.active_crystal.decision_id, | |
| timestamp=time.time(), | |
| component=component, | |
| decision_type=decision_type, | |
| inputs=inputs if isinstance(inputs, dict) else {"raw": str(inputs)}, | |
| reasoning=reasoning, | |
| outcome=outcome, | |
| ) | |
| self.trace_buffer.append(trace) | |
| self._buffer_line(trace.to_json()) | |
| def log_crystal(self, crystal: DecisionCrystal): | |
| if self.disabled: | |
| return | |
| self._buffer_line(crystal.crystallize()) | |
| def start_phase(self, phase_name: str, _context: Any): | |
| self.log_decision( | |
| phase_name, | |
| "PHASE_START", | |
| {"timestamp": time.time()}, | |
| "Phase execution initiated.", | |
| "RUNNING", | |
| ) | |
| def end_phase(self, phase_name: str, _ctx_before: Any, _ctx_after: Any): | |
| self.log_decision( | |
| phase_name, | |
| "PHASE_END", | |
| {"timestamp": time.time()}, | |
| "Phase execution completed.", | |
| "SUCCESS", | |
| ) | |
| def finalize_cycle(self): | |
| if self.active_crystal: | |
| self.log_crystal(self.active_crystal) | |
| self.active_crystal = None | |
| self.flush_to_disk() | |
| def _buffer_line(self, json_str: str): | |
| if self.disabled: | |
| return | |
| self.write_buffer.append(json_str) | |
| if len(self.write_buffer) >= self.BUFFER_SIZE: | |
| self.flush_to_disk() | |
| def flush_to_disk(self): | |
| if self.disabled or not self.current_trace_file or not self.write_buffer: | |
| return | |
| try: | |
| with open(self.current_trace_file, "a", encoding="utf-8") as f: | |
| f.write("\n".join(self.write_buffer) + "\n") | |
| self.write_buffer.clear() | |
| self.write_errors = 0 | |
| except IOError as e: | |
| self.write_errors += 1 | |
| if self.write_errors >= 5: | |
| print(f"{Prisma.RED}[TELEMETRY]: Critical write failure threshold reached. Telemetry disabled. {e}{Prisma.RST}") | |
| self.disabled = True | |
| self.write_buffer.clear() | |
| else: | |
| keep_count = self.BUFFER_SIZE // 2 | |
| self.write_buffer = self.write_buffer[-keep_count:] | |
| print(f"{Prisma.RED}[TELEMETRY]: Write error ({self.write_errors}). Retrying later. {e}{Prisma.RST}") | |
| def read_recent_history(self, limit=4) -> List[str]: | |
| if not os.path.exists(self.log_dir): | |
| return [] | |
| pattern = os.path.join(self.log_dir, "trace_*.jsonl") | |
| files = sorted(glob.glob(pattern), key=os.path.getmtime, reverse=True) | |
| history = [] | |
| for fpath in files: | |
| if len(history) >= limit: | |
| break | |
| try: | |
| with open(fpath, "r", encoding="utf-8") as f: | |
| lines = deque(f, maxlen=limit * 2) | |
| for line in reversed(lines): | |
| if len(history) >= limit: | |
| break | |
| try: | |
| data = json.loads(line) | |
| if ( | |
| data.get("_type") == "CRYSTAL" | |
| or "final_response" in data | |
| ): | |
| resp = data.get("final_response", "") | |
| if not resp: | |
| continue | |
| prompt = data.get("prompt_snapshot", "") | |
| user_text = "Unknown" | |
| if "User:" in prompt: | |
| parts = prompt.split("User:") | |
| if len(parts) > 1: | |
| user_text = parts[1].split("\n")[0].strip() | |
| entry = f"User: {user_text} | System: {resp}" | |
| history.insert(0, entry) | |
| except json.JSONDecodeError: | |
| continue | |
| except Exception: | |
| continue | |
| return history[-limit:] | |
| def get_last_thoughts(self, limit=3) -> List[str]: | |
| history = self.read_recent_history(limit) | |
| return [h.split("System: ")[-1] for h in history if "System: " in h] | |
| def get_last_fatal_error(self) -> Optional[str]: | |
| pattern = os.path.join(self.log_dir, "trace_*.jsonl") | |
| files = sorted(glob.glob(pattern), key=os.path.getmtime, reverse=True) | |
| if len(files) < 2: | |
| return None | |
| prev_file = files[1] | |
| try: | |
| with open(prev_file, "r") as f: | |
| lines = f.readlines() | |
| if not lines: | |
| return None | |
| last_line = json.loads(lines[-1]) | |
| if "outcome" in last_line and "CRITICAL" in str(last_line["outcome"]): | |
| return f"PREVIOUS SYSTEM CRASH: {last_line.get('reasoning', 'Unknown')}" | |
| except Exception: | |
| return None | |
| def generate_session_summary(self, _uptime: float = 0.0) -> str: | |
| self.flush_to_disk() | |
| count = len(self.trace_buffer) | |
| status = "DISABLED" if self.disabled else "ACTIVE" | |
| return ( | |
| f"\n[TELEMETRY] Session ended ({status}). {count} crystals crystallized.\n" | |
| f" Trace: {self.current_trace_file}" | |
| ) | |