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
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This repository contains a **QLoRA fine-tuned adapter** for [CohereLabs/tiny-aya-earth](https://huggingface.co/CohereLabs/tiny-aya-earth) (3.35B parameters), specialized on the **Yorùbá (`yo`)** instruction-following split of `masakhane/african-ultrachat`.
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Developed as part of the KubeCon + CloudNativeCon Europe 2027 case study:
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> **"Serving Sovereign AI: Deploying Quantized, Localized LLMs on Resource-Constrained Kubernetes"**
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## Model Details
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- **Developer:** Hussein Alamutu
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- **Base Architecture:** Dense decoder-only transformer with Grouped Query Attention (GQA), 36 layers, 8k context window.
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This repository contains a **QLoRA fine-tuned adapter** for [CohereLabs/tiny-aya-earth](https://huggingface.co/CohereLabs/tiny-aya-earth) (3.35B parameters), specialized on the **Yorùbá (`yo`)** instruction-following split of `masakhane/african-ultrachat`.
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## Model Details
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- **Developer:** Hussein Alamutu
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- **Base Architecture:** Dense decoder-only transformer with Grouped Query Attention (GQA), 36 layers, 8k context window.
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