Instructions to use mikav-ai/mikav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikav-ai/mikav with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mikav-ai/mikav", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Mikav
Mikav is an open-source Malayalam foundation model project built for Hugging Face.
Goals
- Train a Malayalam-first language model.
- Keep data preparation, tokenizer training, model training, and evaluation reproducible.
- Publish model checkpoints, tokenizer files, and model cards on Hugging Face.
Repository Layout
configs/contains tokenizer, model, pretraining, and fine-tuning settings.data/contains raw, cleaned, deduplicated, and tokenized datasets.tokenizer/contains tokenizer assets and training scripts.mikav/contains model and tokenizer Python package code.scripts/contains data, training, evaluation, and Hugging Face upload scripts.training/contains distributed training configs.evaluation/contains benchmark and prompt evaluation assets.model_card/contains Hugging Face model-card documentation.examples/contains inference examples.tests/contains basic checks.
Quick Start
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
License
Apache-2.0 by default. Update LICENSE if you choose a different license.
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