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"
File size: 2,144 Bytes
9fd3d5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | {
"status": "PASS_ACTUAL_SOURCE_LAZY_STRUCTURAL_LOAD",
"recorded_at": "2026-09-21T17:51:07.992995+00:00",
"platform": "Linux x86_64",
"source": "<SOURCE_MODEL_DIR>",
"source_repo": "ukisai/Swift-1.5-Qwen3.8-27b",
"source_revision": "00ccd14e006897d28cb0ed5bf26390e60d274251",
"source_verification": "compatibility/aws-source-verification.json",
"source_verification_sha256": "ad944ae15bedaaec80abf9cd5e9e945f478397328cf3145f19517ff96295ad9c",
"source_tensors": 1199,
"mapped_tensors": 1199,
"categories": {
"text": 851,
"MTP": 15,
"vision": 333
},
"ignored_tensors": 0,
"unexplained_tensors": 0,
"weight_backing": "actual verified source safetensors; lazy loading",
"full_parameter_evaluation": false,
"tokenizer": "Qwen2Tokenizer",
"chat_templates": [
{
"options": {
"enable_thinking": false
},
"tokens": 15,
"rendered": "<|im_start|>user\nSay hello.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
},
{
"options": {
"reasoning_effort": "low"
},
"tokens": 43,
"rendered": "<|im_start|>system\nReasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.<|im_end|>\n<|im_start|>user\nSay hello.<|im_end|>\n<|im_start|>assistant\n<think>\n"
},
{
"options": {
"reasoning_effort": "xhigh"
},
"tokens": 55,
"rendered": "<|im_start|>system\nReasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.<|im_end|>\n<|im_start|>user\nSay hello.<|im_end|>\n<|im_start|>assistant\n<think>\n"
}
],
"processor": "Qwen3VLProcessor",
"generation": "NOT_RUN",
"mtp_runtime": "component-only; no integrated speculative decoding",
"vision_runtime": "encoder-only; no integrated multimodal generation",
"quantization_executed": false,
"elapsed_seconds": 0.9277446469999973,
"mlx_active_memory_bytes": 8,
"process_peak_rss_bytes": 797855744
}
|