Instructions to use ukisai/Swift-1.5-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

Swift-1.5-4bit-MLX / compatibility /mac-compatibility-results.json

ukisai

initial release

9fd3d5f 12 days ago

Raw History Blame

1.92 kB

	{
	"status": "PASS_MAC_NATIVE_MLX_FORMAT_AND_REAL_METAL_SAMPLES",
	"platform": "macOS-26.6-arm64-arm-64bit",
	"machine": "arm64",
	"mlx": "0.32.2",
	"mlx_lm": "0.32.0",
	"device": "Device(gpu, 0)",
	"quantization": {
	"group_size": 64,
	"bits": 4,
	"mode": "affine"
	},
	"all_saved_tensor_headers_validated": 2379,
	"all_source_parameters_accounted_for": 1199,
	"strict_complete_parameter_tree": "PASS using unevaluated header fixtures; no fabricated weights saved",
	"actual_checkpoint_samples": [
	{
	"sample": 0,
	"category": "text",
	"checkpoint_weight": "language_model.model.layers.0.linear_attn.in_proj_a.weight",
	"original_shape": [
	48,
	5120
	],
	"packed_shape": [
	48,
	640
	],
	"native_metal_bf16": "PASS",
	"metal_fp32": "PASS",
	"bf16_max_absolute_error": 0.0004401206970214844,
	"fp32_max_absolute_error": 0.0
	},
	{
	"sample": 1,
	"category": "vision",
	"checkpoint_weight": "visual.blocks.0.attn.proj.weight",
	"original_shape": [
	1152,
	1152
	],
	"packed_shape": [
	1152,
	144
	],
	"native_metal_bf16": "PASS",
	"metal_fp32": "PASS",
	"bf16_max_absolute_error": 0.0002315044403076172,
	"fp32_max_absolute_error": 0.0
	},
	{
	"sample": 2,
	"category": "MTP",
	"checkpoint_weight": "mtp.layers.0.self_attn.k_proj.weight",
	"original_shape": [
	1024,
	5120
	],
	"packed_shape": [
	1024,
	640
	],
	"native_metal_bf16": "PASS",
	"metal_fp32": "PASS",
	"bf16_max_absolute_error": 0.0009589195251464844,
	"fp32_max_absolute_error": 0.0
	}
	],
	"full_model_mac_generation": "NOT_RUN: targeted native Metal format and real-weight component checks only",
	"peak_mlx_bytes": 4597960,
	"peak_process_rss_bytes": 360693760
	}