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
| """ | |
| 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() | |
| 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] | |