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

Libraries

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

aedmark commited on Feb 24

Commit

8f97c6d

verified ·

1 Parent(s): 086f3b7

Update README.md

Browse files

# 🍄 BoneAmanita: The CryoSomatic Hypervisor

**BoneAmanita** is an experimental text-adventure engine and interactive philosophical companion. 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:

1. **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.
2. **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](https://ollama.com/) installed and running.

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

Files changed (1) hide show

README.md +10 -3

README.md CHANGED Viewed

@@ -1,3 +1,10 @@
----
-license: unlicense
----

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