Native by default
Built on Apple’s MLX framework. Unified memory, Metal acceleration, and a workflow tuned for M-series machines.
Open source · Apple silicon native
Fine-tuning,
without the cloud.
MLX-LM-LoRA puts serious language-model adaptation on your Mac. Fast, private, and built for the silicon under your desk.
MLX-LM-LoRA is trusted by teams and industry leaders such as:
MacPawTypeFoxComputacenter
Also being used by researchers, engineers, and other professionals at Apple, IBM, Bosch, Red Hat, Hugging Face, Daimler Truck, and Mercedes-Benz Group.
A focused training stack for people who want to adapt models locally—without turning every experiment into a distributed-systems project. MLX-LM-LoRA is the code. MLX-LoRA-Studio is the calm, visual layer on top. The JOSIE model family is a production, very high-intelligence model family trained on MLX-LM-LoRA. Learn more about JOSIE ↗
Small surface.
Deep control.
Everything you need to move from dataset to adapter, with the defaults and visibility to understand what is happening.
LoRA, made legible
Low-rank adaptation with explicit configuration, checkpoints, evaluation hooks, and reproducible command-line runs.
Studio when you want it
Launch and monitor jobs through MLX-LoRA-Studio: a native interface for datasets, hyperparameters, logs, and exports.
Training
algorithms.
Choose from 12+ training algorithms in one local package—the first and only training package in its class to bring this breadth together. Each algorithm includes advanced features and deep customization for the job at hand.
SFT
Supervised fine-tuningDPO
Direct preference optimizationCPO
Contrastive preferenceORPO
Odds-ratio preferenceGRPO
Group relative policyGSPO
Group sequence policyOnline DPO
Online preference trainingPPO
Proximal policy optimizationFTPO
Final-token preferenceQAT
Quantization-aware trainingRLHF Reinforce
KL-regularized RL+ more
Extensible training modes
Supervised fine-tuningDPO
Direct preference optimizationCPO
Contrastive preferenceORPO
Odds-ratio preferenceGRPO
Group relative policyGSPO
Group sequence policyOnline DPO
Online preference trainingPPO
Proximal policy optimizationFTPO
Final-token preferenceQAT
Quantization-aware trainingRLHF Reinforce
KL-regularized RL+ more
Extensible training modes
First run
From zero
to adapter.
Install the toolkit, point it at a compatible model and dataset, then keep the whole loop on your machine.
View document ↓# install the toolkit
pip install mlx-lm-lora
# train a LoRA adapter
mlx_lm_lora train
--model mlx-community/Llama-3.2-3B-Instruct-4bit
--data ./data
--iters 600
# install the studio
Open the MLX-LoRA-Studio GitHub repo
Download the .dmg and install it
Two ways in