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 file_server.py from jlind456/jason-ai-twin: direct link, hf CLI and curl.
- Browser
- Download file 943 Bytes
-
https://huggingface.co/jlind456/jason-ai-twin/resolve/main/file_server.py
- Command line
-
hf download hf://jlind456/jason-ai-twin/file_server.py
-
curl -L -o file_server.py https://huggingface.co/jlind456/jason-ai-twin/resolve/main/file_server.py
943 Bytes
| import os | |
| from mcp.server.fastmcp import FastMCP | |
| # Initialize a secure local file server | |
| mcp = FastMCP("local-files") | |
| SHARE_DIR = "/projects" | |
| def list_files() -> str: | |
| """List all files available in the shared folder.""" | |
| try: | |
| files = os.listdir(SHARE_DIR) | |
| return "\n".join(files) if files else "The directory is empty." | |
| except Exception as e: | |
| return f"Error listing files: {str(e)}" | |
| def read_file(filename: str) -> str: | |
| """Read the full text content of a specific file inside the shared folder.""" | |
| try: | |
| # Path validation: strips directories to block path traversal hacks (e.g., ../../../etc/passwd) | |
| safe_name = os.path.basename(filename) | |
| target_path = os.path.join(SHARE_DIR, safe_name) | |
| with open(target_path, "r", encoding="utf-8") as f: | |
| return f.read() | |
| except Exception as e: | |
| return f"Error reading file: {str(e)}" | |