Instructions to use HusseinAlamutu/tiny-aya-earth-yoruba-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HusseinAlamutu/tiny-aya-earth-yoruba-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-earth") model = PeftModel.from_pretrained(base_model, "HusseinAlamutu/tiny-aya-earth-yoruba-lora") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- yo
license: apache-2.0
tags:
- sovereign-ai
- yoruba
- peft
- lora
- tiny-aya
- kubernetes
base_model: CohereLabs/tiny-aya-earth
pipeline_tag: text-generation
datasets:
- masakhane/african-ultrachat
TinyAya-Earth Yoruba LoRA Adapter
This repository contains a QLoRA fine-tuned adapter for CohereLabs/tiny-aya-earth (3.35B parameters), specialized on the Yorùbá (yo) instruction-following split of masakhane/african-ultrachat.
Model Details
- Developer: Hussein Alamutu
- Base Architecture: Dense decoder-only transformer with Grouped Query Attention (GQA), 36 layers, 8k context window.
- Base Model:
CohereLabs/tiny-aya-earth - Fine-Tuning Dataset:
masakhane/african-ultrachat(Yoruba split) - Training Method: QLoRA (4-bit NF4 base, rank 16, alpha 32)
- Primary Language: Yoruba (
yo) - License: Apache 2.0
- GitHub Repository: https://github.com/husseinalamutu/sovereign-k8s-llm
Intended Use
Designed for offline, sovereign inference on resource-constrained Kubernetes infrastructure when quantized to GGUF format and served via llama.cpp.