Instructions to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF", filename="LFM2.5-1.2B-Instruct-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF 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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with Ollama:
ollama run hf.co/FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
- Unsloth Studio
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF 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/Vitalis_LFM2.5_Cortex.GGUF 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/Vitalis_LFM2.5_Cortex.GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF to start chatting
- Pi
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF 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/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF: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/Vitalis_LFM2.5_Cortex.GGUF with Docker Model Runner:
docker model run hf.co/FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
- Lemonade
How to use FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FerrellSyntheticIntelligence/Vitalis_LFM2.5_Cortex.GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Vitalis_LFM2.5_Cortex.GGUF-Q4_K_M
List all available models
lemonade list
d2a5f5a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | """
Cryptographic Episodic Ledger — Vitalis FSI
High-performance, append-only, thread-safe, HMAC-signed memory ledger.
Uses line-delimited JSON (JSONL) to achieve O(1) append efficiency.
"""
from __future__ import annotations
import hashlib
import hmac
import json
import os
import secrets
import time
import fcntl
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Optional
from src.cognition._constants import BASE_DIR, logger
from src.cognition.serialization import VitalisEncoder
LEDGER_DIR = BASE_DIR / "ledger"
LEDGER_DIR.mkdir(parents=True, exist_ok=True)
KEY_FILE = LEDGER_DIR / ".vitalis_hmac.key"
if not KEY_FILE.exists():
KEY_FILE.write_bytes(secrets.token_bytes(32))
HMAC_SECRET = KEY_FILE.read_bytes()
@dataclass(slots=True)
class LedgerBlock:
index: int
timestamp: float
task_id: str
outcome_metrics: str
previous_hash: str
block_hash: str = field(init=False)
signature: str = field(init=False)
def __post_init__(self) -> None:
self.block_hash = self._calc_hash()
self.signature = self._sign_hash()
def _calc_hash(self) -> str:
data = f"{self.index}|{self.timestamp:.6f}|{self.task_id}|{self.outcome_metrics}|{self.previous_hash}"
return hashlib.sha256(data.encode("utf-8")).hexdigest()
def _sign_hash(self) -> str:
return hmac.new(HMAC_SECRET, self.block_hash.encode("utf-8"), hashlib.sha256).hexdigest()
def is_valid(self) -> bool:
if self.block_hash != self._calc_hash():
return False
expected = hmac.new(HMAC_SECRET, self.block_hash.encode("utf-8"), hashlib.sha256).hexdigest()
return hmac.compare_digest(self.signature, expected)
class QuantumResistantLedger:
"""Streamlined append-only ledger processing system."""
def __init__(self, ledger_file: str = "primary_chain.jsonl"):
self.chain_path: Path = LEDGER_DIR / ledger_file
self.chain: List[LedgerBlock] = []
self._load_chain()
def _load_chain(self) -> None:
if not self.chain_path.exists():
self._create_genesis_block()
return
with open(self.chain_path, "r", encoding="utf-8") as f:
fcntl.flock(f.fileno(), fcntl.LOCK_SH) # Shared read lock
try:
for line in f:
line = line.strip()
if not line:
continue
block_dict = json.loads(line)
block = LedgerBlock(
index=block_dict["index"],
timestamp=block_dict["timestamp"],
task_id=block_dict["task_id"],
outcome_metrics=block_dict["outcome_metrics"],
previous_hash=block_dict["previous_hash"],
)
block.block_hash = block_dict["block_hash"]
block.signature = block_dict.get("signature", "")
self.chain.append(block)
finally:
fcntl.flock(f.fileno(), fcntl.LOCK_UN)
logger.debug("Ledger loaded – %d blocks parsed.", len(self.chain))
def _create_genesis_block(self) -> None:
genesis = LedgerBlock(
index=0,
timestamp=time.time(),
task_id="GENESIS_0000",
outcome_metrics="INITIALIZATION",
previous_hash="0" * 64,
)
self.chain.append(genesis)
# Initial write requires file generation
with open(self.chain_path, "w", encoding="utf-8") as f:
fcntl.flock(f.fileno(), fcntl.LOCK_EX)
try:
json_str = json.dumps(genesis, cls=VitalisEncoder, ensure_ascii=False)
f.write(json_str + "\n")
finally:
fcntl.flock(f.fileno(), fcntl.LOCK_UN)
logger.info("Genesis block synchronized successfully.")
def append_record(self, task_id: str, outcome_metrics: str) -> LedgerBlock:
"""Appends a single ledger line in O(1) time without rewriting historical lines."""
last = self.chain[-1]
new_block = LedgerBlock(
index=last.index + 1,
timestamp=time.time(),
task_id=task_id,
outcome_metrics=outcome_metrics,
previous_hash=last.block_hash,
)
json_str = json.dumps(new_block, cls=VitalisEncoder, ensure_ascii=False)
# Open in append mode 'a' - will NOT truncate file before lock execution
with open(self.chain_path, "a", encoding="utf-8") as f:
fcntl.flock(f.fileno(), fcntl.LOCK_EX)
try:
f.write(json_str + "\n")
f.flush()
try:
os.fsync(f.fileno())
except Exception:
pass
finally:
fcntl.flock(f.fileno(), fcntl.LOCK_UN)
self.chain.append(new_block)
logger.info("Ledger appended – block %d (task %s)", new_block.index, task_id)
return new_block
def verify_integrity(self) -> bool:
"""Validates chronological continuity and hardware hmac signatures."""
for i in range(1, len(self.chain)):
cur = self.chain[i]
prev = self.chain[i - 1]
if cur.previous_hash != prev.block_hash:
logger.error("Ledger structural broken at block %d", i)
return False
if not cur.is_valid():
logger.error("Signature invalid at block %d. Modification detected.", i)
return False
logger.info("Ledger integrity verified – %d blocks validated.", len(self.chain))
return True
def latest_block(self) -> LedgerBlock:
return self.chain[-1]
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