Instructions to use AlphaOxO/Lizzy-7B-8bits-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlphaOxO/Lizzy-7B-8bits-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AlphaOxO/Lizzy-7B-8bits-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use AlphaOxO/Lizzy-7B-8bits-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AlphaOxO/Lizzy-7B-8bits-MLX"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AlphaOxO/Lizzy-7B-8bits-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use AlphaOxO/Lizzy-7B-8bits-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "AlphaOxO/Lizzy-7B-8bits-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "AlphaOxO/Lizzy-7B-8bits-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlphaOxO/Lizzy-7B-8bits-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use AlphaOxO/Lizzy-7B-8bits-MLX with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AlphaOxO/Lizzy-7B-8bits-MLX"
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 AlphaOxO/Lizzy-7B-8bits-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AlphaOxO/Lizzy-7B-8bits-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AlphaOxO/Lizzy-7B-8bits-MLX"
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 "AlphaOxO/Lizzy-7B-8bits-MLX" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
| #!/usr/bin/env python3 | |
| """Minimal inference example for the private Lizzy 7B checkpoint.""" | |
| from __future__ import annotations | |
| import os | |
| def main() -> None: | |
| repo_id = os.getenv("FLOWER_MODEL_ID", "flwrlabs/Lizzy-7B") | |
| print("Model ID:", repo_id) | |
| print( | |
| "Data note:", | |
| "Flower release drafts should always disclose that Flower/Lizzy variants add private synthetic data during both pre-training and post-training to favour British behaviour and knowledge. Those private synthetic datasets are not redistributed in the release pack.", | |
| ) | |
| print("HF_TOKEN present:", bool(os.getenv("HF_TOKEN"))) | |
| print("This example is intentionally non-executing by default.") | |
| print("Use one of the snippets below after installing transformers or vLLM:") | |
| print() | |
| print("Transformers:") | |
| print( | |
| " tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)" | |
| ) | |
| print( | |
| " model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True, torch_dtype='auto')" | |
| ) | |
| print() | |
| print("vLLM:") | |
| print( | |
| " python -m vllm.entrypoints.openai.api_server --model " | |
| "flwrlabs/Lizzy-7B --trust-remote-code --max-model-len 8192" | |
| ) | |
| if __name__ == "__main__": | |
| main() | |