# How to train and fine-tune your own LLM on a Mac | PTLLM > Practical guide to train your own specialist LLM on Apple Silicon: define a target, LoRA/QLoRA post-train, evaluate against a frozen baseline, and package… Canonical: https://posttrainllm.com/fine-tune-llm-on-mac Practical guide to train your own specialist LLM on Apple Silicon: define a target, LoRA/QLoRA post-train, evaluate against a frozen baseline, and package with evidence. Fine-tuning an LLM on a Mac is practical when the target is narrow, the model fits Apple Silicon memory, and the evaluation is designed before training begins. The useful goal is rarely to build a smaller general chatbot. It is to create a specialist that improves on a defined task, routes away from tasks it should not answer, and ships with evidence about both gains and regressions. ## Start with a target, not a trainer Write down the task, input and output contract, baseline model, acceptance slices, and failure conditions. A target such as file operations, SQL hygiene, or intent routing can be evaluated. “Make the model smarter” cannot. Freeze a holdout before generating or cleaning training data. ## Choose a method that fits the Mac LoRA updates a small set of adapter weights while leaving the base model frozen. QLoRA applies low-rank adaptation to a quantized base and can reduce memory pressure further. Full-weight fine-tuning is possible for smaller models but is not the default starting point for a one-machine workflow. Apple’s MLX LM supports text generation, quantization, LoRA, QLoRA, and full fine-tuning on Apple Silicon. ## Build the factory loop - **Target:** define the behavior and routing boundary. - **Data:** create train, validation, and frozen test splits with provenance. - **Post-train:** run a bounded recipe and preserve the configuration. - **Evaluate:** compare the unchanged baseline and candidate on the same gates. - **Package:** export the adapter or fused model with runtime metadata. - **Report:** publish gains, regressions, missing measurements, and the ship, retry, or reject decision. PostTrainLLM implements this loop as a Mac-local specialist factory. Its public evidence includes specialist packages and report cards, including a file-operations specialist that improves its hard gate from 58% to 100% while regressing out-of-domain breadth from 59.6% to 42.3%. That tradeoff is why it is routed rather than presented as a general planner. ## What success looks like Success is a reproducible candidate that beats the frozen baseline on the target, stays within explicit regression limits, fits the intended runtime, and has enough evidence to make a decision. A falling training loss is only an intermediate signal. **CTA:** Inspect the public specialist artifacts, then follow the MLX LoRA and evaluation guides. ## Continue through the factory - [mlx lora fine tuning](https://posttrainllm.com/mlx-lora-fine-tuning) - [evaluate local llm](https://posttrainllm.com/evaluate-local-llm) - [agent trajectory dataset](https://posttrainllm.com/agent-trajectory-dataset) - [build small language model specialist](https://posttrainllm.com/build-small-language-model-specialist) - [posttrainllm vs unsloth](https://posttrainllm.com/posttrainllm-vs-unsloth) - [posttrainllm vs axolotl](https://posttrainllm.com/posttrainllm-vs-axolotl) - [artifacts](https://posttrainllm.com/artifacts) - [recipes](https://posttrainllm.com/recipes)