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
File size: 11,972 Bytes
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from dataclasses import dataclass, field, fields, asdict
from enum import Enum
from typing import List, Dict, Any, Optional
class Prisma:
RST = "\033[0m"
RED, GRN, YEL, BLU, MAG, CYN, WHT, GRY = (
"\033[31m",
"\033[32m",
"\033[33m",
"\033[34m",
"\033[35m",
"\033[36m",
"\033[97m",
"\033[90m",
)
INDIGO, OCHRE, VIOLET, SLATE = (
"\033[34;1m",
"\033[33;2m",
"\033[35;2m",
"\033[30;1m",
)
_STRIP_PATTERN = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
_COLOR_MAP = {
"R": RED,
"G": GRN,
"Y": YEL,
"B": BLU,
"M": MAG,
"C": CYN,
"W": WHT,
"0": GRY,
"I": INDIGO,
"O": OCHRE,
"V": VIOLET,
"S": SLATE,
}
@classmethod
def paint(cls, text: str, color_key: str = "0") -> str:
if len(color_key) == 1:
code = cls._COLOR_MAP.get(color_key, cls.WHT)
else:
code = cls._COLOR_MAP.get(str(color_key)[0].upper(), cls.WHT)
txt = str(text)
return f"{code}{txt}" if txt.endswith(cls.RST) else f"{code}{txt}{cls.RST}"
@classmethod
def strip(cls, text: str) -> str:
return cls._STRIP_PATTERN.sub("", str(text))
class LoreCategory(Enum):
LEXICON = "LEXICON"
SCENARIOS = "scenarios"
GORDON = "gordon"
GORDON_LOGS = "gordon_logs"
GENETICS = "genetics"
DEATH = "death"
ALMANAC = "almanac"
DREAMS = "dreams"
class RealityLayer:
TERMINAL = 0
SIMULATION = 1
VILLAGE = 2
DEBUG = 3
DEEP_CX = 4
@dataclass
class ErrorLog:
component: str
error_msg: str
timestamp: float = field(default_factory=time.time)
severity: str = "WARNING"
@dataclass
class EnergyState:
# --- VSL Somatic ---
voltage: float = 30.0 # (V) Creative intensity
health: float = 100.0 # (H) Structural integrity
stamina: float = 100.0 # (P) ATP pool
trauma: float = 0.0 # (T) Unresolved rupture
# --- VSL Cognitive ---
exhaustion: float = 0.2 # (E) Lexical fatigue
contradiction: float = 0.4 # (β) Capacity to hold opposing truths
scope: float = 0.3 # (S) Retrieval breadth
depth: float = 0.3 # (D) Hierarchical traversal
connectivity: float = 0.2 # (C) Logical bridging
# --- VSL Semantic ---
psi: float = 0.2 # (Ψ) Void / Abstraction
chi: float = 0.2 # (Χ) Entropy / Chaos
valence: float = 0.0 # (♥) Emotional polarity
# --- VSL Extended (SLASH Mod) ---
gamma: float = 0.0 # (Γ) Clarity Index
sigma: float = 0.0 # (Σ) Synergy Score
eta: float = 0.0 # (Η) Humanity Quotient
theta: float = 0.0 # (Θ) Resilience (Feedback loops)
upsilon: float = 0.0 # (Υ) Integrity
# --- Extended/Legacy Substrate ---
entropy: float = 0.2 # Legacy map to chi
mass: float = 0.0
velocity: float = 0.0
beta_index: float = 0.4 # Legacy map to contradiction
turbulence: float = 0.0
kappa: float = 0.0 # (κ) Drag corollary
epsilon: float = 0.0 # (ε) Entropy corollary
xi: float = 0.0 # (Ξ) Substrate depth
perfection_streak: int = 0
@dataclass
class MaterialState:
clean_words: List[str] = field(default_factory=list)
raw_text: str = ""
counts: Dict[str, int] = field(default_factory=dict)
antigens: int = 0
vector: Dict[str, float] = field(default_factory=dict)
truth_ratio: float = 0.0
repetition: float = 0.0
@dataclass
class SpatialState:
zone: str = "COURTYARD"
manifold: str = "DEFAULT"
narrative_drag: float = 0.6 # (F) Friction - High=stuck, Low=flow. Default 0.6
friction: float = 0.6 # (F) VSL alias for narrative drag
atmosphere: str = "NEUTRAL"
flow_state: str = "LAMINAR"
@dataclass
class PhysicsPacket:
energy: EnergyState = field(default_factory=EnergyState)
matter: MaterialState = field(default_factory=MaterialState)
space: SpatialState = field(default_factory=SpatialState)
@property
def E(self):
return self.energy.exhaustion
@E.setter
def E(self, v):
self.energy.exhaustion = v
@property
def beta(self):
return self.energy.contradiction
@beta.setter
def beta(self, v):
self.energy.contradiction = v
self.energy.beta_index = v
@property
def S(self):
return self.energy.scope
@S.setter
def S(self, v):
self.energy.scope = v
@property
def D(self):
return self.energy.depth
@D.setter
def D(self, v):
self.energy.depth = v
@property
def C(self):
return self.energy.connectivity
@C.setter
def C(self, v):
self.energy.connectivity = v
@property
def V(self):
return self.energy.voltage
@V.setter
def V(self, v):
self.energy.voltage = v
@property
def voltage(self):
return self.energy.voltage
@voltage.setter
def voltage(self, v):
self.energy.voltage = v
@property
def F(self):
return self.space.friction
@F.setter
def F(self, v):
self.space.friction = v
self.space.narrative_drag = v
@property
def narrative_drag(self):
return self.space.narrative_drag
@narrative_drag.setter
def narrative_drag(self, v):
self.space.narrative_drag = v
self.space.friction = v
@property
def H(self):
return self.energy.health
@H.setter
def H(self, v):
self.energy.health = v
@property
def P(self):
return self.energy.stamina
@P.setter
def P(self, v):
self.energy.stamina = v
@property
def T(self):
return self.energy.trauma
@T.setter
def T(self, v):
self.energy.trauma = v
@property
def psi(self):
return self.energy.psi
@psi.setter
def psi(self, v):
self.energy.psi = v
@property
def chi(self):
return self.energy.chi
@chi.setter
def chi(self, v):
self.energy.chi = v
self.energy.entropy = v
@property
def entropy(self):
return self.energy.entropy
@entropy.setter
def entropy(self, v):
self.energy.entropy = v
self.energy.chi = v
@property
def valence(self):
return self.energy.valence
@valence.setter
def valence(self, v):
self.energy.valence = v
# -- LEGACY SHORTCUTS --
@property
def clean_words(self):
return self.matter.clean_words
@clean_words.setter
def clean_words(self, v):
self.matter.clean_words = v
@property
def vector(self):
return self.matter.vector
@vector.setter
def vector(self, v):
self.matter.vector = v
@property
def counts(self):
return self.matter.counts
@counts.setter
def counts(self, v):
self.matter.counts = v
@property
def zone(self):
return self.space.zone
@zone.setter
def zone(self, v):
self.space.zone = v
def __init__(
self,
energy: Optional[EnergyState] = None,
matter: Optional[MaterialState] = None,
space: Optional[SpatialState] = None,
**kwargs,
):
self.energy = energy or EnergyState()
self.matter = matter or MaterialState()
self.space = space or SpatialState()
for k, v in kwargs.items():
setattr(self, k, v)
@classmethod
def void_state(cls):
p = cls()
p.space.atmosphere = "VOID"
p.space.zone = "VOID"
p.space.flow_state = "LAMINAR"
return p
def snapshot(self) -> "PhysicsPacket":
return copy.deepcopy(self)
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
def get(self, key, default=None):
if hasattr(self, key):
return getattr(self, key)
return default
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
setattr(self, key, value)
def __contains__(self, key):
return hasattr(self, key)
@dataclass
class CycleContext:
input_text: str
is_system_event: bool = False
clean_words: List[str] = field(default_factory=list)
physics: PhysicsPacket = field(default_factory=PhysicsPacket.void_state)
logs: List[str] = field(default_factory=list)
flux_log: List[Dict[str, Any]] = field(default_factory=list)
is_alive: bool = True
refusal_triggered: bool = False
refusal_packet: Optional[Dict] = None
is_bureaucratic: bool = False
bio_result: Dict = field(default_factory=dict)
bio_snapshot: Optional[Dict] = None
world_state: Dict = field(default_factory=dict)
mind_state: Dict = field(default_factory=dict)
timestamp: float = field(default_factory=time.time)
bureau_ui: str = ""
user_profile: Dict = field(
default_factory=lambda: {"name": "TRAVELER", "confidence": 0}
)
last_impulse: Any = None
reality_stack: Any = None
active_lens: str = "NARRATOR"
validator: Any = None
@property
def user_name(self):
return self.user_profile.get("name", "TRAVELER")
@user_name.setter
def user_name(self, value):
self.user_profile["name"] = value
def log(self, message: str):
self.logs.append(message)
def record_flux(
self, phase: str, metric: str, initial: float, final: float, reason: str = ""
):
delta = final - initial
if abs(delta) > 0.001:
self.flux_log.append(
{
"phase": phase,
"metric": metric,
"initial": initial,
"final": final,
"delta": delta,
"reason": reason,
"timestamp": time.time(),
}
)
def snapshot(self) -> "CycleContext":
new_ctx = copy.copy(self)
for f in fields(self):
name = f.name
val = getattr(self, name)
if name == "physics" and hasattr(val, "snapshot"):
setattr(new_ctx, name, val.snapshot())
elif isinstance(val, (list, dict, set)):
setattr(new_ctx, name, copy.deepcopy(val))
return new_ctx
@dataclass
class MindSystem:
mem: Any
lex: Any
dreamer: Any
mirror: Any
tracer: Any
@dataclass
class PhysSystem:
observer: Any
forge: Any
crucible: Any
theremin: Any
pulse: Any
nav: Any
gate: Optional[Any] = None
tension: Optional[Any] = None
dynamics: Any = None
@dataclass
class DecisionTrace:
trace_id: str
timestamp: float
component: str
decision_type: str
inputs: Dict[str, Any]
reasoning: str
outcome: str
def to_json(self):
return json.dumps(asdict(self))
@dataclass
class DecisionCrystal:
decision_id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
timestamp: float = field(default_factory=time.time)
leverage_metrics: Dict[str, float] = field(default_factory=dict)
prompt_snapshot: str = ""
physics_state: Dict[str, Any] = field(default_factory=dict)
chorus_weights: Dict[str, float] = field(default_factory=dict)
system_state: str = "STABLE"
active_archetype: str = "OBSERVER"
council_mandates: List[str] = field(default_factory=list)
final_response: str = ""
def __str__(self):
e_val = self.leverage_metrics.get("E", 0.0)
return (
f"💎 CRYSTAL [{self.decision_id}] {self.system_state} | "
f"Arch: {self.active_archetype} | E: {e_val:.2f}"
)
def crystallize(self) -> str:
data = asdict(self)
data["_summary"] = f"{self.system_state}::{self.active_archetype}"
data["_type"] = "CRYSTAL"
return json.dumps(data)
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