Instructions to use millat/laya-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use millat/laya-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download millat/laya-mlx --local-dir laya-mlx
- Laya
How to use millat/laya-mlx with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download NOTICE from millat/laya-mlx: direct link, hf CLI and curl.
- Browser
- Download file 698 Bytes
-
https://huggingface.co/millat/laya-mlx/resolve/main/NOTICE
- Command line
-
hf download hf://millat/laya-mlx/NOTICE
-
curl -L -o NOTICE https://huggingface.co/millat/laya-mlx/resolve/main/NOTICE
698 Bytes
| laya-mlx | |
| Copyright 2026 laya-mlx contributors | |
| This product includes software derived from Laya: | |
| https://github.com/NandhaKishorM/laya | |
| Copyright Convai Innovations and Laya contributors. Licensed under Apache-2.0. | |
| Upstream source revision: 6a5819129eb220570792e417e49723d697efd76f | |
| The token sequence construction, question rendering, confidence calculation, | |
| presets, email utilities and language router are adapted from Laya. | |
| The neural network is reimplemented using Apple's MLX, following Laya's | |
| DecisionModel and the ModernBERT architecture in Hugging Face Transformers. | |
| Model weights are downloaded separately from Convai Innovations on Hugging Face; | |
| they are not included in this repository. | |