Troy
Fine-tune LLMs on your MacBook with one YAML file.
Troy is a command-line tool for fine-tuning and preference-tuning language models locally on Apple Silicon. No CUDA, no cloud, no training pipeline — write a config, run one command, and train on the machine you already own.
Built on MLX and mlx-lm, Apple's ML framework for Apple Silicon. Unified memory means a 36 GB MacBook fine-tunes models that need a workstation GPU anywhere else.
Requirements
- Apple Silicon Mac (M1 or later)
- macOS 14+
- Python 3.10–3.12
Install
brew install avirajkhare00/troy/troy
# or from source: pip install ./cli
Quickstart
troy doctor # check your Mac: chip, memory, MLX, what you can train
troy init # create troy.yaml + sample data
troy train # fine-tune (LoRA/QLoRA via MLX)
troy chat # talk to the result
troy serve # OpenAI-compatible API at localhost:8080/v1
troy export -f gguf # ship it to llama.cpp / Ollama / LM Studio
The config is the interface
base: mlx-community/Qwen3-0.6B-4bit
task: sft # or: dpo
data:
train: ./data/train.jsonl # alpaca, sharegpt, chat, completions, text — auto-detected
val_split: 0.1
training:
epochs: 3
lr: 1e-5
batch_size: auto # sized from your Mac's unified memory
lora:
r: 8
alpha: 16
output: ./output
Commands
| Command | Purpose |
|---|---|
troy init |
Create a config from a template (chat, dpo) |
troy doctor |
Hardware + dependency check, with model-size guidance |
troy train |
LoRA fine-tuning: SFT or DPO |
troy chat |
Interactive REPL (or -p for one-shot) with your adapter |
troy serve |
OpenAI-compatible API server for your model |
troy export |
Fuse the adapter; export MLX or GGUF |
troy data inspect |
Dataset stats and format detection |
What Troy can train on your Mac
| Unified memory | Max model (4-bit QLoRA) |
|---|---|
| 8 GB | ~1.5B |
| 16 GB | ~4B |
| 24 GB | ~8B |
| 36 GB | ~14B |
| 64 GB | ~32B |
| 128 GB | ~70B |
DPO without a second model
DPO normally keeps a frozen reference copy of the model in memory. Troy zeroes the LoRA scales to recover the reference model from the policy model itself — no second copy, which matters on unified memory.
Data formats
Auto-detected from the first record: Alpaca (instruction/output),
ShareGPT (conversations), chat (messages), prompt/completion,
plain text, and preference pairs (prompt/chosen/rejected) for DPO.
Files: .jsonl, .json, .csv.
License
Apache-2.0
1 maintainer
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