Instructions to use jlind456/jason-ai-twin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jlind456/jason-ai-twin 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 jlind456/jason-ai-twin
# Run inference directly in the terminal:
llama cli -hf jlind456/jason-ai-twin

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jlind456/jason-ai-twin
# Run inference directly in the terminal:
llama cli -hf jlind456/jason-ai-twin

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 jlind456/jason-ai-twin
# Run inference directly in the terminal:
./llama-cli -hf jlind456/jason-ai-twin

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 jlind456/jason-ai-twin
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jlind456/jason-ai-twin

Use Docker

docker model run hf.co/jlind456/jason-ai-twin

LM Studio
Jan

vLLM

How to use jlind456/jason-ai-twin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jlind456/jason-ai-twin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jlind456/jason-ai-twin",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jlind456/jason-ai-twin

Ollama
How to use jlind456/jason-ai-twin with Ollama:
```
ollama run hf.co/jlind456/jason-ai-twin
```

Unsloth Studio

How to use jlind456/jason-ai-twin 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 jlind456/jason-ai-twin 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 jlind456/jason-ai-twin to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jlind456/jason-ai-twin to start chatting

How to use jlind456/jason-ai-twin with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jlind456/jason-ai-twin

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": "jlind456/jason-ai-twin"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jlind456/jason-ai-twin with Docker Model Runner:
```
docker model run hf.co/jlind456/jason-ai-twin
```

Lemonade

How to use jlind456/jason-ai-twin with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jlind456/jason-ai-twin

Run and chat with the model

lemonade run user.jason-ai-twin-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use jlind456/jason-ai-twin with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jlind456/jason-ai-twin

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 jlind456/jason-ai-twin

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jlind456/jason-ai-twin with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jlind456/jason-ai-twin

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 "jlind456/jason-ai-twin" \
  --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"

Jason AI Twin

Local, offline digital twin model and voice cloning repository.

Models and Voice Files

digital_twin_q4.gguf: The quantized local LLM brain (jason_twin).
cloned_output.wav: Cloned voice reference audio for XTTS v2 text-to-speech.
my_voice_clean.wav: Clean source audio reference for voice synthesis.
amy.onnx / amy.onnx.json: Cloned voice model for Piper TTS.
chat_twin.py: Interactive CLI interface for AI Twin chat and speech.

Quickstart

Running the AI Twin CLI:

python3 chat_twin.py

Running with Ollama:

ollama create jason_twin -f Modelfile
ollama run jason_twin

Downloads last month: 100

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants