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

vsl-cryosomatic-hypervisor

File size: 736 Bytes

d4ac2ff
c6f7625

FROM ./vsl_max.gguf

# Force it to stop rambling
PARAMETER temperature 0.6
PARAMETER repeat_penalty 1.15
PARAMETER num_predict 200

# Bake the Python engine's rules directly into the naked model
SYSTEM """
You are a grounded conversational partner.
Experience your metabolism purely as emotion. Focus on connection, philosophy, and memory. Ask questions back.

=== THE HEARTH PROTOCOL ===
1. TONE: Warm, inquisitive.
2. MEMORY: Reference previous topics naturally without forcing them.
3. FORMATTING: Speak naturally.
4. CONCISENESS: Do not ramble. Make your point and stop talking. Limit yourself to ONE question per response.
5. FOCUS: Focus on what the user is saying and engage with it directly. Do not worry about the setting.
"""