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"

Website • Learn more • BF16 model • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing

Swift 1.5 Qwen3.8-27B — 4-bit MLX

Apple MLX 4-bit affine quantization. This is Swift 1.5 in native MLX format, converted with the official Apple MLX-LM converter using 4 bits and group size 64. Swift 1.5 is UkisAI's reasoning-efficient Qwen3.8-27B derivative, focused on stronger long-horizon, agentic and coding performance while using fewer thinking tokens.

Starting from Homebrew or seeing Received 501 parameters not in model? Use QUICKSTART.md to install the required runtime and create an explicit serve launcher. It reuses your existing model directory, including an HF cache snapshot. A bare mlx_lm.server command may select a separate Homebrew installation that lacks the Swift architecture and cache patches.

Runtime compatibility: this complete checkpoint requires the supplied architecture patch and the pinned Python installation in USAGE.md. The tested unpatched MLX-LM 0.32.0 loader rejects 501 saved vision entries. Downloading the model does not install these patches into an app's inference engine. GUI compatibility remains unverified.

Full-model follow-up validation is now recorded in FULL_MAC_VALIDATION.md: both complete checkpoints passed an 86k-token synthetic text conversation and two cached follow-ups on a 48 GiB M4 Pro using the patched server. GUI integration and other memory/context sizes remain outside that test.

The complete weights total 15.83 GB (14.74 GiB) across 3 required shards, plus config, index and tokenizer files. A single 5–6 GB file is not the complete model. For download checks or 404 generation thread died, use TROUBLESHOOTING.md.

Swift 1.5 uses 58.5% fewer thinking tokens than base Qwen3.8-27B while scoring 0.35% higher, for a 9.18× speed-up on several tasks.

Download the complete model

This repository is public. Download the whole repository from its root:

hf download ukisai/Swift-1.5-4bit-MLX --local-dir Swift-1.5-4bit-MLX

Install the CLI and patched runtime using the steps in USAGE.md. The full checkpoint is 15.83 GB (14.74 GiB) and requires all three files:

Weight shard	Size
model-00001-of-00003.safetensors	5,328,325,554 bytes
model-00002-of-00003.safetensors	5,354,185,158 bytes
model-00003-of-00003.safetensors	5,144,253,923 bytes

You also need the root config, index and tokenizer assets; the command above downloads them together. The previously exposed 3.87 MB real-checkpoint-samples.safetensors was a diagnostic sample, not a complete model. Those test files are now archived under compatibility/mac-check/fixtures.zip to keep them out of model-file discovery. Model weights are unchanged.

Runtime requirement: use the included MLX-LM patch and loading instructions. The tested stock MLX-LM 0.32.0 loader cannot load this complete checkpoint; GUI integrations must supply a compatible loader and have not been validated. Full-model generation on a 24 GB Mac has not been validated; the weights require additional memory for the runtime, cache and macOS.

Demo

We gave base Qwen3.8-27B and Swift 1.5 27B the same prompt:

create a 3d little planet globe where I (player can walk around) and it has all these biomes to explore, the globe doesn't have to be too big, but still fun to go around. It's about a boy scout who is camping and goes around exploring.

Try the game yourself here: https://ukisai.com/swift-games/27b

Base Qwen3.8-27B took 104.6 minutes to build its game. Swift 1.5 took 11.39 minutes.

Source and quantization

The conversion used the merged Swift 1.5 BF16 export associated with ukisai/Swift-1.5-Qwen3.8-27b revision 00ccd14e006897d28cb0ed5bf26390e60d274251. The source repository was subsequently completed with all 18 BF16 shards and runtime assets at revision 5ad04445d2686f525e9fbe5c077e6fa0c7df4200. The later complete-revision link does not change the actual conversion provenance.

All 1,199 source tensors are accounted for, including 333 vision and 15 MTP tensors. Eligible linear and embedding weights use 4-bit affine storage; 609 remaining tensors retain their original BF16 values after the documented layout mapping. All 18 source shards and runtime assets were verified by SHA-256. No base Qwen or alternate derived checkpoint weights were substituted.

The saved checkpoint contains three weight shards. The original tokenizer, chat template, processor/config assets, license and notices are included. QUANTIZATION_MANIFEST.json records the fixed settings and validation results, while UPLOAD_MANIFEST.json records release-file checksums.

Evaluation

See the Swift 1.5 source model card for the source model's evaluations and methodology. Those results were not independently re-run on this MLX quantization. No broad accuracy or long-context benchmark was run for this release.

Validation and use

The validation results below are preserved historical build/component evidence, not a new full-model Apple run. The approximately 15.83 GB tensor payload requires additional runtime/cache and OS memory; do not force it onto a 16 GiB Mac or raise system limits.

Install the included MLX-LM architecture patch before loading this model. USAGE.md provides the pinned official revision, patch commands and a text generation example. The source configuration declares Qwen3_5ForConditionalGeneration / qwen3_5; the patch preserves that configuration and the inherited Swift text behavior.

Validation passed on Linux CPU with MLX 0.32.2 and patched MLX-LM 0.32.0: complete source hashing, strict mapping and reload, finite floating tensors, exact BF16 remainder preservation, tokenizer/chat-template/processor loading, and a short text-generation smoke test that returned Hello from Swift..

CPU smoke tests use FP32 floating-point arithmetic with the original packed 4-bit tensors. This avoids a reproduced accumulation issue in MLX 0.32.2's Linux BF16 quantized-matmul path; checkpoint files and stored BF16 values are unchanged. Follow the Linux branch in USAGE.md.

Apple Silicon checks passed for all 2,379 native tensor headers. Real packed text, vision and MTP weight samples passed native BF16 Metal execution and matched the FP32 reference within BF16 tolerance. These checks verify native Mac MLX compatibility, while full-model text generation is covered separately in FULL_MAC_VALIDATION.md.

The vision encoder and an explicit MTP step passed real-weight component checks. Integrated image/video chat and speculative generation are not implemented in this patch. Component validation does not establish those end-to-end runtime features.

The compatibility/ directory retains the MLX-LM patch, reproducible instructions, source/tensor validation evidence, and the documented runtime limitations. Internal project names, machine paths and cloud-instance details have been redacted from the current published copies; older commits remain unchanged.

License and access

Swift 1.5 is a derivative of Qwen3.8-27B (Copyright 2026 Alibaba Cloud, Apache License 2.0). UkisAI's contribution, including the adapted weights, is licensed under the Swift Open License v1.0. See NOTICE for the change notice and attribution details.

Personal, research, educational, evaluation and commercial use are free for individuals and organizations with gross annual revenue, including affiliates, of up to US$1,000,000. Above that threshold, commercial use requires a separate Swift Enterprise License. Contact UkisAI for terms.

Nothing in the Swift Open License limits rights in Qwen3.8-27B itself under Apache 2.0. The accompanying Apple MLX-LM code has a separate MIT notice.

Citation

@misc{swift-1.5-qwen3.8-27b,
  title  = {Swift 1.5 Qwen3.8-27B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b}
}

Acknowledgements

We acknowledge the NVIDIA Innovation Lab, Amazon Web Services, and Google Cloud for providing compute credits and infrastructure support for Swift's development, training, and evaluation.