Instructions to use ukisai/Swift-1.5-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ukisai/Swift-1.5-4bit-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("ukisai/Swift-1.5-4bit-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 ukisai/Swift-1.5-4bit-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 "ukisai/Swift-1.5-4bit-MLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ukisai/Swift-1.5-4bit-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use ukisai/Swift-1.5-4bit-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 "ukisai/Swift-1.5-4bit-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ukisai/Swift-1.5-4bit-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ukisai/Swift-1.5-4bit-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use ukisai/Swift-1.5-4bit-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 "ukisai/Swift-1.5-4bit-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 ukisai/Swift-1.5-4bit-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ukisai/Swift-1.5-4bit-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 "ukisai/Swift-1.5-4bit-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 "ukisai/Swift-1.5-4bit-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"
Add a verified Swift runtime installer and explicit server launcher
A bare mlx_lm.server command can select Homebrew's separate, unpatched installation. That loader rejects the complete Swift checkpoint with Received 501 parameters not in model for vision entries, even when the correct model snapshot is already cached.
Add a small Python 3.12 setup helper that reuses the local model directory, installs the existing pinned official MLX-LM source and checksum-verified Swift patches into a separate environment, and creates an explicit serve launcher. The launcher verifies architecture/server source hashes and package versions before loading weights, bypasses PATH/PYTHONPATH conflicts, and serves local files with offline mode enabled. Quick-start and troubleshooting links cover the cached-download case.
Validation passed on Apple Silicon: two clean installs (4-bit and 5-bit), actual launcher HTTP generation with tiny existing text/vision/MTP fixtures, conflicting executable and Python search paths, paths with spaces, rejection of an unpatched loader, and refusal to overwrite an existing runtime. Recorded results are in compatibility/launcher-validation.json. These are local checks, not a hosted CI badge or a new full-model run. Earlier complete-model validation remains in FULL_MAC_VALIDATION.md.
No checkpoint weights, quantization, architecture patch, cache patch or tokenizer/config assets change. No requantization or model download is required for an existing complete snapshot. This PR is ready for review; public main is unchanged until merged.