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 Desktop

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"

jason-ai-twin / Emily_Knowledge_Base.py

jlind456

Initial upload of Qwen AI twin weights

9507c73 verified 2 months ago

Raw History Blame Contribute Delete

761 Bytes

	from flask import Flask, request, jsonify
	import os

	app = Flask(__name__)
	AUTHORIZED_DIR = os.path.expanduser("~/robot_share/")

	@app.route('/save_memory', methods=['POST'])
	def save_memory():
	data = request.json
	filename = data.get('filename')
	content = data.get('content')

	# SECURITY GATEKEEPER:
	# Only allow files within robot_share, prevent directory traversal
	safe_filename = os.path.basename(filename)
	full_path = os.path.join(AUTHORIZED_DIR, safe_filename)

	with open(full_path, "w", encoding="utf-8") as f:
	f.write(content)

	return jsonify({"status": "success", "file": safe_filename})

	if __name__ == '__main__':
	# Run this on your local network
	app.run(host='0.0.0.0', port=5000)