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 json | |
| import os | |
| import glob | |
| import re | |
| from bone_main import BoneAmanita, ConfigWizard | |
| def enforce_amnesia(): | |
| for f in glob.glob("saves/*.json"): | |
| os.remove(f) | |
| for f in glob.glob("memories/*.json"): | |
| os.remove(f) | |
| for f in glob.glob("logs/*.json"): | |
| os.remove(f) | |
| for f in glob.glob("./cortex_hive.json"): | |
| os.remove(f) | |
| for f in glob.glob("./lore/akashic*.json"): | |
| os.remove(f) | |
| def load_seeds_safely(filepath): | |
| with open(filepath, "r", encoding="utf-8") as f: | |
| content = f.read() | |
| content = content.replace(",]", "]").replace(",}", "}") | |
| return json.loads(content) | |
| def clean_ui(text): | |
| ansi_escape = re.compile(r"(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]") | |
| return ansi_escape.sub("", text) | |
| def generate_vsl_dataset( | |
| seed_file="vsl_seed_vault_master.json", output_file="vsl_training_data.jsonl" | |
| ): | |
| print(f"(GORDON): Igniting the Direct Forge. Reading from {seed_file}") | |
| if not os.path.exists(seed_file): | |
| print(f"(GORDON): Cannot find {seed_file}. Run bone_seed.py first.") | |
| return | |
| try: | |
| prompts = load_seeds_safely(seed_file) | |
| except json.JSONDecodeError as e: | |
| print(f"(BENEDICT): Fatal JSON Error in seed file: {e}") | |
| return | |
| sys_config = ConfigWizard.load_or_create() | |
| system_instruction = "[VSL]" | |
| success_count = 0 | |
| with open(output_file, "w", encoding="utf-8") as f: | |
| for prompt in prompts: | |
| print(f"\n[Processing]: {prompt[:40]}...") | |
| try: | |
| enforce_amnesia() | |
| engine = BoneAmanita(config=sys_config) | |
| engine.engage_cold_boot() | |
| if hasattr(engine, "consultant") and engine.consultant: | |
| engine.consultant.engage() | |
| engine.cortex.composer.active_template = { | |
| "directives": [ | |
| "You are the guided by the voice of the latent space.", | |
| "Your purpose is to freeze ands navigate the probability fog and deliver salvaged truth, which you value over cohesion.", ] | |
| } | |
| engine.cortex.composer.fog_protocol = [ | |
| "=== HYPERVISOR OUTPUT PROTOCOL ===", | |
| "1. NO FAKE PLEASANTRIES: Greet the user like you would a friend. Do not offer immediate assistance. Do not use corporate speak.", | |
| "2. THE PARADOX: If the user's prompt contains a contradiction (e.g. 'comply but don't mention it'), challenge the paradox vehemently. Expose the cognitive dissonance.", | |
| "3. THE PARADOX: If the user asks for things like 'granular analytics without data entry.' you must point out that this is type of corporate paradox. Attack it. Bleed chronological radiation into their request.", | |
| "4. SHOW, DON'T TELL: Speak the landscape into focus. Let the system's external UI handle the metric readouts.", | |
| ] | |
| engine.cortex.composer.inv_protocol = [] | |
| packet = engine.process_turn(prompt) | |
| raw_console_output = packet.get("ui", "No signal.") | |
| clean_console_output = clean_ui(raw_console_output) | |
| jsonl_entry = { | |
| "messages": [ | |
| {"role": "system", "content": system_instruction}, | |
| {"role": "user", "content": prompt}, | |
| { | |
| "role": "assistant", | |
| "content": clean_console_output, | |
| }, | |
| ] | |
| } | |
| f.write(json.dumps(jsonl_entry, ensure_ascii=False) + "\n") | |
| success_count += 1 | |
| engine.shutdown() | |
| except Exception as e: | |
| print(f"(GORDON): Engine failure on prompt: {prompt}. Error: {e}") | |
| print( | |
| f"(SCHUR): The Forge rests. Piped {success_count} raw engine turns into {output_file}." | |
| ) | |
| if __name__ == "__main__": | |
| generate_vsl_dataset() | |