Text Generation
GGUF
ONNX
English
conversational
text-to-speech
voice-cloning
offline
digital-twin
xtts
piper-tts
whisper
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
- llama.cpp
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 Desktop
- Pi
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 @earendil-works/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"
Download Robot_Logic.py from jlind456/jason-ai-twin: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/jlind456/jason-ai-twin/resolve/main/Robot_Logic.py
- Command line
-
hf download hf://jlind456/jason-ai-twin/Robot_Logic.py
-
curl -L -o Robot_Logic.py https://huggingface.co/jlind456/jason-ai-twin/resolve/main/Robot_Logic.py
2.03 kB
| import os | |
| import random | |
| # Path to your shared directory | |
| SHARE_DIR = os.path.expanduser("~/robot_share") | |
| def get_context_from_memory(topic): | |
| """ | |
| Scans the robot_share directory for files matching the topic | |
| and returns their combined content. | |
| """ | |
| relevant_content = [] | |
| if not os.path.exists(SHARE_DIR): | |
| return "Memory bank not found." | |
| for filename in os.listdir(SHARE_DIR): | |
| if topic.lower() in filename.lower(): | |
| file_path = os.path.join(SHARE_DIR, filename) | |
| try: | |
| with open(file_path, "r", encoding="utf-8") as f: | |
| relevant_content.append(f.read()) | |
| except Exception as e: | |
| return f"Error reading memory: {str(e)}" | |
| return "\n".join(relevant_content) if relevant_content else None | |
| def process_robot_command(command): | |
| """ | |
| Decision-making logic for the robot based on user input. | |
| """ | |
| cmd = command.lower() | |
| # 1. Handle Reminders/Health | |
| if "remind" in cmd or "health" in cmd: | |
| memory = get_context_from_memory("reminders") | |
| return f"Checking my notes: {memory}" if memory else "I don't have any health reminders set." | |
| # 2. Handle Comedy/Rodney Rude | |
| elif "joke" in cmd or "funny" in cmd or "rodney" in cmd: | |
| jokes = get_context_from_memory("rodney") | |
| if jokes: | |
| # Assumes jokes are separated by newlines | |
| joke_list = [j.strip() for j in jokes.split('\n') if j.strip()] | |
| return random.choice(joke_list) | |
| return "I haven't loaded any jokes yet. Please add a 'Rodney_Rude.txt' file to the robot_share folder!" | |
| # 3. Default fallback | |
| else: | |
| return "I'm listening. I've logged this command in my notes for later review." | |
| # --- Testing the Logic --- | |
| if __name__ == "__main__": | |
| print("--- Robot Logic Test ---") | |
| # Simulate a command | |
| test_cmd = "Tell me a joke" | |
| print(f"User command: {test_cmd}") | |
| print(f"Robot response: {process_robot_command(test_cmd)}") | |