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suraj10620/stark-1.5b
stark-1.5b is a text generation model from suraj10620. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
<p align="center" <img src="logo.png" width="140" alt="Stark logo: a gold bolt inside an arc-reactor ring" </p
Downloads · 30 days
22
5% of all-time downloads
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1.5B
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How the weights are stored.
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From the Hugging Face model README
Grammarly, but better. And it never leaves your Mac.
The model behind Stark, a macOS menu-bar app that rewrites your text fully offline. Select text anywhere, press a hotkey, and the rewrite replaces your selection in place. No account, no cloud, no subscription. Your typos never leave the building.

It's a small model with one job: you give it a one-word style tag and some text, and it answers with the rewritten text and nothing else. No "Here's your polished version!" preamble, no quotes, no commentary.
| Tag | What it does |
|---|---|
polish | fixes grammar and flow, keeps your meaning and tone |
concise | says the same thing in fewer words |
formal | professional tone |
friendly | warm, casual tone |
typos | spelling only, never rephrases |
bullets | turns prose into a markdown bullet list |
prompt | sharpens a vague LLM prompt into a precise one |
expand | grows a terse note into a fuller message: same meaning, no invented facts |
pip install mlx-lm
mlx_lm.generate --model suraj10620/stark-1.5b \
--system-prompt "polish" \
--prompt "i tested the fix on staging and it seem to working fine now"
The style tag goes in the system message, your text in the user message. That's the whole interface. If you've read API docs longer than this model card, that's the point.
On an M-series Mac it uses about 1 GB of RAM and streams at ~97 tokens/sec with ~0.1 s to first token, fast enough that rewrites feel instant. It's a 1.5B model, not a lawyer: an occasional typo slips through, and the odd rewrite drifts. For everyday messages, it does the job.
<details> <summary><b>Appendix: technical details</b></summary>mlx-community/Qwen2.5-1.5B-Instruct-4bit (QLoRA on the quantized
model), fused into this standalone model with mlx_lm fuse.typos. No user or
customer data. The seeded generator and training script are in the
GitHub repo (model/), so
the model is reproducible end-to-end.Works with the standard mlx_lm OpenAI-compatible server:
mlx_lm.server --model suraj10620/stark-1.5b --port 8765
curl -s localhost:8765/v1/chat/completions -d '{
"messages": [{"role":"system","content":"concise"},
{"role":"user","content":"I just wanted to quickly reach out to ask whether..."}],
"temperature": 0.2, "max_tokens": 512
}'