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Troy
Fine-tune LLMs on your MacBook with one YAML file
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.
Hey Product Hunt 👋
I built Troy because fine-tuning felt way harder than it needed to be. Every guide assumed a CUDA GPU or a cloud account, while the M-series MacBook on my desk sat there with unified memory that can comfortably train a 14B model.
Troy is a CLI that makes local fine-tuning boring, in the best way:
- One YAML file is the whole interface. Base model, data path, LoRA params — that's it. troy train does the rest.
- No dataset? Synthesize one. troy data synth --from ./docs uses a local teacher model to write training data from your own files.
- Close the loop on-device. troy chat to talk to the result, troy eval for base-vs-tuned comparison, troy serve for an OpenAI-compatible API at localhost.
- Ship it anywhere. Export to GGUF for Ollama/LM Studio, push to Hugging Face, or troy export -f ios to package the model for iPhone/iPad apps.
- Troy Mesh (my favorite part): idle iPhones on your Wi-Fi join as workers and generate training data with a local teacher model while your Mac coordinates and trains. Your phone literally writes the dataset.
It supports SFT, DPO, ORPO, and vision-language models, and runs any architecture mlx-lm supports (Llama, Qwen, Gemma, Phi, Mistral…). A 36 GB MacBook handles models that need a workstation GPU anywhere else.
Install: brew install avirajkhare00/troy/troy
It's open source (Apache-2.0). I'd love to hear what you'd fine-tune first — and if you hit anything weird, troy doctor output in an issue gets you a fast fix. 🙏
About Troy on Product Hunt
“Fine-tune LLMs on your MacBook with one YAML file”
Troy was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #77 on the daily leaderboard. 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.
On the analytics side, Troy competes within Open Source, Artificial Intelligence, GitHub and Apple — topics that collectively have 604.9k followers on Product Hunt. The dashboard above tracks how Troy performed against the three products that launched closest to it on the same day.
Who hunted Troy?
Troy was hunted by Aviraj Khare. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Troy including community comment highlights and product details, visit the product overview.
Aviraj Khare