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* feature: LoRA adapter for Embeddings * feature: wire in LoRAEmbedding into the tuner. Allow the embedding and non model.layers Linear layers to be targeted for fine tuning * feature: DoRA adapter for Embeddings * feature: wire in DoRAEmbedding * bugfix: ensure self.m is recalculated when the linear layer is changed in DoRALinear.from_linear * refactor: prefer from_base over from_linear or from_embedding. prefer fuse over to_linear or to_embedding * cleanup: remove unused imports in test_dora.py * refactor: remove unnecessary non_layer_modules * cleanup: remove wrong comments for lora embedding dropout. remove uncessary parens in dora embedding dropout * nits --------- Co-authored-by: Awni Hannun <awni@apple.com>
MLX Examples
This repo contains a variety of standalone examples using the MLX framework.
The MNIST example is a good starting point to learn how to use MLX.
Some more useful examples are listed below.
Text Models
- MLX LM a package for LLM text generation, fine-tuning, and more.
- Transformer language model training.
- Minimal examples of large scale text generation with LLaMA, Mistral, and more in the LLMs directory.
- A mixture-of-experts (MoE) language model with Mixtral 8x7B.
- Parameter efficient fine-tuning with LoRA or QLoRA.
- Text-to-text multi-task Transformers with T5.
- Bidirectional language understanding with BERT.
Image Models
- Image classification using ResNets on CIFAR-10.
- Generating images with Stable Diffusion or SDXL.
- Convolutional variational autoencoder (CVAE) on MNIST.
Audio Models
- Speech recognition with OpenAI's Whisper.
Multimodal models
- Joint text and image embeddings with CLIP.
- Text generation from image and text inputs with LLaVA.
- Image segmentation with Segment Anything (SAM).
Other Models
- Semi-supervised learning on graph-structured data with GCN.
- Real NVP normalizing flow for density estimation and sampling.
Hugging Face
Note: You can now directly download a few converted checkpoints from the MLX Community organization on Hugging Face. We encourage you to join the community and contribute new models.
Contributing
We are grateful for all of our contributors. If you contribute to MLX Examples and wish to be acknowledged, please add your name to the list in your pull request.
Citing MLX Examples
The MLX software suite was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find MLX Examples useful in your research and wish to cite it, please use the following BibTex entry:
@software{mlx2023,
author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
url = {https://github.com/ml-explore},
version = {0.0},
year = {2023},
}