⚡ All JANG models are meant to be run in vMLX

Osaurus-AppleScript-8B-JANG_6M

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

Built by Osaurus. Base: Zyphra/ZAYA1-8B (MoE). Quantized with JANG (6-bit affine, MLX-native) for fast on-device inference via MLX. Verified in the Osaurus runtime.

Parameters	8.84 B (MoE, 16 experts)
Quant	JANG 6-bit — 6-bit affine weights + 8-bit embeddings (MLX mx.quantize, group 32)
Size	~7.4 GB
Tool-calling	native (<zyphra_tool_call>), run_applescript
Runtime	MLX (Apple Silicon) / Osaurus

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

Tool-call emission = emits a run_applescript call. Compile = the emitted script is valid AppleScript. Exec = it runs and returns the correct result (scored on the pure-computational subset with a deterministic answer).

	Tool-call emission	Compile	Exec
Base (Zyphra/ZAYA1-8B)	✗ (writes raw text, no tool calls)	28.9%	30.0%
Osaurus-AppleScript-8B-JANG_6M	100% ⭐	80% ⭐	75% ⭐

The fine-tune teaches structured tool-calling and valid AppleScript (base does neither). Typical app-automation tool-calls sit at the compile tier; the exec column is the hardest computational subset (string/number algorithms, shell, coercions).

Usage (MLX, tool-calling)

from mlx_lm import load, generate
model, tok = load("OsaurusAI/Osaurus-AppleScript-8B-JANG_6M")
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":"Get the URL of the front Safari tab."}]
prompt = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True, tokenize=False)
print(generate(model, tok, prompt=prompt, max_tokens=300))
# -> <zyphra_tool_call><function=run_applescript><parameter=script> ... </parameter>...

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

For higher accuracy use OsaurusAI/Osaurus-AppleScript-16B-A4B-JANG_4M. This 8B is the fast/small tier for low-latency on-device automation.

License

Inherits the Zyphra/ZAYA1-8B license — review and comply with the base model's terms.

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Made by Osaurus · contact eric@osaurus.ai