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
| import ast | |
| import os | |
| import json | |
| class SyntaxIngester: | |
| def __init__(self, output_dim=512): | |
| self.output_dim = output_dim | |
| def parse_file_to_ast(self, file_path): | |
| """Reads a local source file and parses it into an Abstract Syntax Tree.""" | |
| if not os.path.exists(file_path): | |
| raise FileNotFoundError(f"Target file not found: {file_path}") | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| source_code = f.read() | |
| try: | |
| return ast.parse(source_code) | |
| except SyntaxError as e: | |
| # Finesse means capturing semantic dissonance early | |
| print(f"[-] Syntax error encountered during parsing: {e}") | |
| return None | |
| def flatten_ast_structure(self, node): | |
| """Recursively flattens the AST into a highly structured logic sequence.""" | |
| sequence = [] | |
| for n in ast.walk(node): | |
| node_type = type(n).__name__ | |
| # Extract structural identity based on node type | |
| if isinstance(n, ast.FunctionDef): | |
| sequence.append({"type": node_type, "identifier": n.name, "args": len(n.args.args)}) | |
| elif isinstance(n, ast.ClassDef): | |
| sequence.append({"type": node_type, "identifier": n.name}) | |
| elif isinstance(n, (ast.If, ast.While, ast.For)): | |
| sequence.append({"type": node_type, "gate_logic": "conditional_branch"}) | |
| elif isinstance(n, ast.Name): | |
| sequence.append({"type": node_type, "id": n.id}) | |
| elif isinstance(n, ast.operator): | |
| sequence.append({"type": "Operator", "op": node_type}) | |
| return sequence | |
| def process_directory(self, dir_path): | |
| """Processes a target directory of clean codebase source files.""" | |
| payload = [] | |
| for root, _, files in os.walk(dir_path): | |
| for file in files: | |
| if file.endswith('.py'): | |
| full_path = os.path.join(root, file) | |
| tree = self.parse_file_to_ast(full_path) | |
| if tree: | |
| structural_profile = self.flatten_ast_structure(tree) | |
| payload.append({ | |
| "source_file": file, | |
| "profile": structural_profile | |
| }) | |
| return payload | |
| if __name__ == "__main__": | |
| # Self-test the ingester on our own runtime modules | |
| ingester = SyntaxIngester() | |
| print("[+] Initializing system self-analysis baseline...") | |
| try: | |
| sample_profile = ingester.process_directory("src/devcore") | |
| print(f"[+] Ingestion complete. Parsed modules: {len(sample_profile)}") | |
| print(json.dumps(sample_profile[:1], indent=2)) | |
| except Exception as e: | |
| print(f"[-] Self-test initialization failed: {e}") | |