Instructions to use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M 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("OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M")

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 OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"

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": "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M 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 "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"

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 OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M

Run Hermes

hermes

OpenClaw new

How to use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"

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 "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M" \
  --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"

MLX LM

How to use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Osaurus-AppleScript-16B-A4B-JANG_4M / README.md

Osaurus-AI

AppleScript tool-calling model (JANG_4M, 4-bit experts, run_applescript)

bf2b623 verified 25 days ago

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	---
	license: gemma
	language:
	- en
	library_name: mlx
	tags:
	- applescript
	- macos
	- computer-use
	- agent
	- tool-calling
	- function-calling
	- mlx
	- moe
	- osaurus
	base_model: google/gemma-4-26B-A4B-it
	pipeline_tag: text-generation
	---

	![Osaurus](./osaurus-x-banner.png)

	# AppleScript-16B-A4B-JANG_4M

	The flagship tool-calling model for macOS AppleScript & agentic computer-use. Given a
	`run_applescript` tool, it emits a structured tool call with correct AppleScript to drive macOS —
	app automation (Safari, Finder, Mail, Notes, Calendar, Reminders, System Events), system control,
	clipboard, screenshots, ASObjC, and `do shell script` — ready to execute in an agent loop. Without a
	tools spec, it writes AppleScript directly (hybrid).

	Built by [Osaurus](https://osaurus.ai). Derived from gemma-4-26B-A4B (MoE): expert-pruned to a
	hyper-focused 16.1 B (~4 B active), fine-tuned for AppleScript + tool-calling, quantized to
	JANG_4M (8-bit attention, 4-bit routed experts) for fast on-device inference via
	[MLX](https://github.com/ml-explore/mlx).

	\| \| \|
	\|---\|---\|
	\| Parameters \| 16.1 B total · ~4 B active (MoE) \|
	\| Quant \| JANG_4M — 8-bit attention, 4-bit routed experts \|
	\| Size \| ~11 GB \|
	\| Tool-calling \| native (gemma tool-call format), `run_applescript` \|
	\| Runtime \| MLX (Apple Silicon) / Osaurus \|

	## Benchmark — base vs final (held-out executable AppleScript bench, 87 tasks)

	Tool-call emission = emits a valid `run_applescript` call. Compile = valid AppleScript. Exec = it
	runs and returns the correct result.

	\| \| Tool-call emission \| Compile \| Exec \|
	\|---\|---\|---\|---\|
	\| Base gemma-4-26B-A4B \| ✗ (writes raw, no tool calls) \| ~88% \| unreliable \|
	\| AppleScript-16B-A4B-JANG_4M \| 100% ⭐ \| 100% ⭐ \| 84% ⭐ \|

	Highest-quality AppleScript model in the Osaurus line: 100% structured tool-call emission, 100% valid
	AppleScript, 84% executable correctness.

	## Usage (MLX, tool-calling)

	```python
	from mlx_lm import load, generate
	model, tok = load("OsaurusAI/AppleScript-16B-A4B-JANG_4M")
	tools = [{"type":"function","function":{"name":"run_applescript",
	"description":"Execute AppleScript on macOS and return its output.",
	"parameters":{"type":"object","properties":{"script":{"type":"string"}},"required":["script"]}}}]
	msgs = [{"role":"user","content":"Create a note titled 'Groceries' in Notes."}]
	prompt = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True, tokenize=False)
	print(generate(model, tok, prompt=prompt, max_tokens=300))
	```

	Your agent parses the `run_applescript` tool call, runs the script (e.g. via `osascript`), and feeds
	the result back. (Omit `tools=` to get raw AppleScript instead.)

	## Tiers

	- AppleScript-16B-A4B-JANG_4M (this) — flagship quality (~11 GB).
	- AppleScript-8B-JANG_4M — fast/small tier (~5.6 GB).

	## License

	Inherits the Gemma license — review and comply with the base model's terms.

	---
	Made by Osaurus · contact eric@osaurus.ai