Text Classification
MLX
Safetensors
Laya
Polish
English
devops
decision-model
apple-silicon
experimental
synthetic-data
claude-code
litellm
Instructions to use tebebe/laya-devops-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tebebe/laya-devops-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download tebebe/laya-devops-mlx --local-dir laya-devops-mlx
- Laya
How to use tebebe/laya-devops-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
File size: 821 Bytes
aa73ff9 | 1 2 3 4 5 6 7 8 9 10 | Laya DevOps v29 — early experimental fine-tune, 2026.
Released under Apache License 2.0; upstream notices remain applicable.
Base MLX checkpoint: aac6fef/laya-multilingual-mlx, revision f2b4faf51023039425946074e2cf1361d2db11d5 (Apache-2.0).
Original Laya multilingual model: Convai Innovations and contributors.
MLX runtime/port: mizorewww/laya-mlx (Apache-2.0).
Training software: biplovgautam/LayaStudio (Apache-2.0), revision 71f1a01466e247c4ac9a8afe681e3491cb0f2e06 plus locally recorded changes.
Encoder: JHU CLSP mmBERT-base (MIT). Its tokenizer originates from Gemma 2; retain upstream tokenizer notices and applicable terms.
Sol and Astra assisted synthetic data and development; their weights are not included.
See licenses/ for upstream license and notice files, and README.md for provenance and limitations.
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