Greninja9257/LabLLM
A Mac app that lets you build and train your own language model from scratch
LabLLM is a native macOS app that runs the whole language-model workflow locally on Apple Silicon, from designing a Transformer to training, fine-tuning, and chatting with it. There is no cloud upload and no subscription; everything stays on the Mac. It is written in Swift and accelerated with Apple's MLX framework.
What it does
- You can design a GPT-style decoder inside the app and feed it data from Hugging Face, local files, or even imported iMessage conversations
- It supports pretraining with AdamW/SGD, warmup, cosine learning-rate schedules, and gradient clipping, plus LoRA and DPO fine-tuning, with live loss curves and generated samples during training
- Trained models can be saved and resumed as checkpoints, exported as 4-bit or 8-bit quantized models, or served locally through an OpenAI-shaped API for immediate chatting
- It includes inspection tools like an 'X-Ray' token viewer showing token probabilities and entropy, and an embedding explorer that projects embeddings into 2D space
- The repository currently has 22 GitHub stars and is described as beta software, built and documented mainly by a single developer so far
Why it matters
It turns the process of building and training a language model into something you can do hands-on with a single Mac, instead of a black box that requires cloud infrastructure. Its local-first design also matters for anyone who wants to experiment with private data without sending it anywhere.
Terms in this repo
- MLX · Apple's framework for running machine-learning computations efficiently on Apple Silicon chips
- Transformer · The neural network architecture that most modern language models are built on
- LoRA · A fine-tuning technique that trains a small set of added parameters instead of the whole model, saving compute
- DPO · A training method that adjusts a model using pairs of preferred and non-preferred responses
- Quantization · Reducing the numerical precision of model weights to shrink size and computation
Repository description (English)
build the architecture, train the weights, and watch a small LLM emerge from scratch, locally on Apple Silicon with custom data, tokenizers, checkpoints, and MLX acceleration.
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