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brain

Fine-tune local models with MLX LoRA on Apple Silicon and export to GGUF.

Updated Apr 11, 2026
Search Tags:#deployment#on-device#finetune#local-models#gguf#apple-silicon#mlx-lora
One-click install
npx skills add https://github.com/jetteim/platform-workstation-bootstrap --skill brain-jetteim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: brain
Source: https://github.com/jetteim/platform-workstation-bootstrap/tree/main/agents/skills/codex-curated/brain
Command: npx skills add https://github.com/jetteim/platform-workstation-bootstrap --skill brain-jetteim

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Local ML pipelines on Apple Silicon often require custom, end-to-end workflows to train and deploy lightweight models that can run offline, protecting privacy and reducing cloud reliance.

Core Features & Use Cases

  • MLX LoRA fine-tuning on Apple Silicon for small-to-mid sized models.
  • GGUF export and deployment via Rust or Python sidecars.
  • Reproducible pipelines for micro-to-medium tasks (classification to generation).
  • Use case: building a private local model for a domain-specific task.

Quick Start

Train a micro brain on a small labeled dataset and deploy it with a Rust or Python sidecar.

Frequently Asked Questions about brain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a local model on Apple Silicon using MLX LoRA?▼

MLX LoRA fine-tuning on Apple Silicon is a technique to adapt small-to-mid sized local models for offline, privacy-preserving inference, reducing cloud reliance while creating reproducible pipelines for domain-specific tasks.

Can I export a fine-tuned model to GGUF for on-device deployment?▼

Yes, this workflow supports GGUF export for fine-tuned models, enabling on-device deployment via Rust binaries or Python sidecars to achieve offline, privacy-preserving inference for micro to medium tasks.

Does MLX LoRA fine-tuning work for building privacy-preserving local models?▼

Yes, MLX LoRA fine-tuning on Apple Silicon supports building privacy-preserving local models by enabling offline training and on-device deployment, ensuring sensitive data never leaves your machine.

What's the best way to create a domain-specific local model without cloud reliance?▼

The best way is an end-to-end MLX LoRA fine-tuning workflow on Apple Silicon, which handles task framing, data preparation, evaluation, and GGUF export to deliver private, offline local models without cloud reliance.

Do I need Apple Silicon to run MLX LoRA fine-tuning pipelines?▼

Yes, MLX LoRA fine-tuning pipelines require Apple Silicon hardware, as the workflow is specifically optimized to build and deploy micro to medium local models on that architecture.

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