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Troy

Fine-tune LLMs on your MacBook with one YAML file

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Troy fine-tunes and preference-tunes LLMs locally on Apple Silicon. Write one YAML file, run troy train, then chat, eval, serve an OpenAI-compatible API, or export to GGUF/Ollama and even iPhone apps. Built on MLX. No CUDA, no cloud, no pipeline.

PH · launched Sep 18, 2026 · 2 upvotes (top 26%) · 0 views

Niche: llm optimization tools · 127 launches, 9 in the last 7 days ▲ +50% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project
  • Open Source
  • Artificial Intelligence
  • GitHub
  • Apple

Visit website ↗ Source listing ↗

Could you build this?

Partial The CLI wrapper and YAML configuration are straightforward, but correctly orchestrating local MLX fine-tuning, LoRA/QLoRA layers, DPO preference tuning, evaluation harnesses, and quantization exports for Apple Silicon requires solid machine learning engineering.

What it would actually take: The tool requires deep integration with Apple's MLX framework in Python or C++, implementing custom LoRA/DoRA adapter layers, gradient accumulation, and loss functions for SFT and DPO algorithms on unified memory architecture. It must handle GGUF conversion, quantizations (AWQ/GPTQ/GGUF formats), and bundle lightweight inference servers mimicking OpenAI's API. Substantial expertise in low-level ML frameworks, memory-bandwidth optimization on Apple Silicon, and modern LLM alignment methods is required.

Competitors

Other products that read as similar to this one : 71 launches clear the similarity bar, closest 8 shown.

Attention rank: #27 of 72 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 262 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.