Instructions to use aedmark/vsl-cryosomatic-hypervisor-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aedmark/vsl-cryosomatic-hypervisor-v3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aedmark/vsl-cryosomatic-hypervisor-v3",
	filename="vsl-max-v3.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-v3 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-v3
# Run inference directly in the terminal:
llama-cli -hf aedmark/vsl-cryosomatic-hypervisor-v3

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-v3
# Run inference directly in the terminal:
llama-cli -hf aedmark/vsl-cryosomatic-hypervisor-v3

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-v3
# Run inference directly in the terminal:
./llama-cli -hf aedmark/vsl-cryosomatic-hypervisor-v3

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-v3
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aedmark/vsl-cryosomatic-hypervisor-v3

Use Docker

docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor-v3

LM Studio
Jan
Ollama
How to use aedmark/vsl-cryosomatic-hypervisor-v3 with Ollama:
```
ollama run hf.co/aedmark/vsl-cryosomatic-hypervisor-v3
```

Unsloth Studio new

How to use aedmark/vsl-cryosomatic-hypervisor-v3 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-v3 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-v3 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-v3 to start chatting

Docker Model Runner
How to use aedmark/vsl-cryosomatic-hypervisor-v3 with Docker Model Runner:
```
docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor-v3
```

Lemonade

How to use aedmark/vsl-cryosomatic-hypervisor-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aedmark/vsl-cryosomatic-hypervisor-v3

Run and chat with the model

lemonade run user.vsl-cryosomatic-hypervisor-v3-{{QUANT_TAG}}

List all available models

lemonade list

vsl-cryosomatic-hypervisor-v3 / README.md

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metadata

license: unlicense
base_model:
  - google/gemma-3-4b-it
  - NousResearch/Hermes-3-Llama-3.1-8B
tags:
  - bonepoke
  - vsl
  - boneamanita

🍄 BoneAmanita: The CryoSomatic Hypervisor v3

BoneAmanita is an experimental text-adventure engine and interactive philosophical companion. It's a hybrid variant of James Taylor's Bonepoke engine and Edmark's own crazy whirlwind of sources. Unlike standard AI wrappers, BoneAmanita embeds a fine-tuned Llama 3 model inside a simulated biological metabolism.

The AI does not just respond to prompts; it feels "Voltage," burns "ATP," accumulates "Trauma," and is governed by a council of internal personas (The SLASH Council). If you try to drink a potion you don't have, the engine will intercept the AI and physically block the action. If you stress the AI out, its text will become fragmented and panicked.

🧠 The Architecture

This project consists of two halves:

The Flesh (GGUF Model): A custom-trained 3B parameter model fine-tuned on highly specific, atmospheric, and philosophical datasets to break the standard "helpful assistant" RLHF.
The Bones (Python Engine): A local terminal interface that tracks inventory, manages the physics/biology simulation, and dynamically injects system constraints into the context window.

🚀 Quickstart Guide

1. Prerequisites

Python 3.10+
Ollama installed and running.

2. Download the Brain Pull the fine-tuned model directly from HuggingFace via Ollama:

ollama pull hf.co/aedmark/vsl-cryosomatic-hypervisor

3. Ignite the Engine

Clone this repository, install the dependencies, and run the main script.

Bash

git clone https://github.com/aedmark/BoneAmanita.git
cd BoneAmanita
python bone_main.py

(On first boot, the ConfigWizard will ask you to set up your profile. Select Ollama as your backend and type hf.co/aedmark/vsl-cryosomatic-hypervisor as the model ID).

🕹️ The Four Realities (Modes)

When you boot the terminal, you will be asked to choose a Reality Mode:

ADVENTURE: A grounded, physical text adventure. Gordon (the inventory manager) will strictly enforce physical reality. You cannot use what you do not have.
CONVERSATION: A purely philosophical, warm dialogue mode. No inventory, no physics, just deep conversation driven by the system's simulated emotional state.
CREATIVE: A high-voltage ideation engine. Dream logic applies.
TECHNICAL: Speak directly to the SLASH Council. Debug the matrix, analyze your metabolism, and write code.

⌨️ Terminal Commands

While inside the simulation, you can use meta-commands:

//layer push [1-4] - Shift reality layers (from literal to abstract).
//reset system - Clear the memory buffer and reset the circuit breaker.
/inventory or /i - Check your pockets.