Instructions to use Outlier-Ai/Outlier-Code-27B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit 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("Outlier-Ai/Outlier-Code-27B-MLX-4bit")

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
LM Studio

How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"

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": "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit 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 "Outlier-Ai/Outlier-Code-27B-MLX-4bit"

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 Outlier-Ai/Outlier-Code-27B-MLX-4bit

Run Hermes

hermes

MLX LM

How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Outlier-Ai/Outlier-Code-27B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Outlier-Code-27B-MLX-4bit / README.md

Matt

drop banned superlative tags pre-launch

46ff895 6 days ago

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metadata

language:
  - en
  - zh
  - fr
  - es
  - pt
  - de
  - it
  - ru
  - ja
  - ko
  - ar
  - vi
  - th
  - nl
  - pl
license: apache-2.0
library_name: mlx
base_model: Qwen/Qwen3.6-27B
tags:
  - 4-bit
  - 4bit
  - agentic
  - apple-silicon
  - chat
  - code
  - code-completion
  - code-generation
  - coding
  - conversational
  - edge-ai
  - function-calling
  - humaneval
  - instruct
  - local-llm
  - m1
  - m2
  - m3
  - m4
  - mac
  - mac-mini
  - mac-studio
  - macbook-air
  - macbook-pro
  - macos
  - metal
  - mlx
  - mlx-lm
  - no-cloud
  - offline
  - on-device
  - outlier
  - outlier-app
  - private
  - private-ai
  - quantized
  - qwen
  - qwen3.6
  - qwen3_5
  - reasoning
  - safetensors
  - text-generation
  - thinking
  - tool-use
pipeline_tag: text-generation
model-index:
  - name: Outlier-Ai/Outlier-Code-27B-MLX-4bit
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HumanEval
          type: openai_humaneval
          split: test
        metrics:
          - type: pass@1
            name: pass@1
            value: 0.8659
            verified: false

Part of the Outlier shipping lineup. Outlier is a free macOS app that runs this model locally, with one click. Apple Silicon only.

Outlier Code 27B (MLX 4-bit)

Code-tuned configuration of the Core 27B weights — same safetensors, different chat template, lower temperature, and code-specialized system prompt. Use this if your primary workflow is code generation or repo-aware editing.

Try it in Outlier

The simplest way to use this model is through the Outlier app — open the tier picker, select Outlier Code, click download, and chat. No setup, no Python, no MLX install, no token quotas.

➡ Download Outlier — outlier.host

A screenshot of the tier picker is at outlier.host/screenshots/tier-picker.png.

Load this directly (power users)

If you want the raw MLX-4bit weights without the app:

pip install mlx-lm
python -m mlx_lm.generate \
  --model Outlier-Ai/Outlier-Code-27B-MLX-4bit \
  --prompt "Write a quicksort in Python." \
  --max-tokens 512

from mlx_lm import load, generate
model, tokenizer = load("Outlier-Ai/Outlier-Code-27B-MLX-4bit")
print(generate(model, tokenizer, prompt="Hello", max_tokens=256))

Verified benchmarks

For σ-qualified MMLU, HumanEval, and Mac inference-speed numbers — with full provenance (source file, command, n, stderr, date) — see outlier.host/benchmarks.

Other Outlier shipping tiers

License

Apache 2.0 (inherits from upstream base model). Conversion artifact only — the underlying weights are governed by the base model's license.