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README.md

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+---
+license: apache-2.0
+language:
+- am
+tags:
+- amharic
+- ethiopian
+- finance
+- lora
+- mlx
+base_model: CohereForAI/aya-expanse-8b
+---
+
+# Tibeb SFT Adapter
+
+LoRA adapters for **Tibeb AI** — Ethiopia's Amharic financial assistant, fine-tuned on Aya Expanse 8B.
+
+## Model Description
+
+Tibeb is an Amharic-language AI assistant focused on financial literacy in the Ethiopian context. These adapters were trained using MLX LoRA on Apple Silicon.
+
+## Training Details
+
+- **Base model:** CohereForAI/aya-expanse-8b (4-bit quantized via mlx-community)
+- **Method:** LoRA (rank 16, all layers, cosine LR decay, AdamW)
+- **Data:** ~692K rows of Amharic instruction data (see dataset repo)
+- **Hardware:** Apple Silicon Mac (MLX backend)
+
+### v3 Config
+- Iterations: 20,000
+- Batch size: 1 (4x gradient accumulation = effective batch 4)
+- Learning rate: 1e-5 → 1e-6 (cosine decay, 500 warmup steps)
+- LoRA rank: 16, dropout: 0.05, scale: 32.0
+- Max sequence length: 512
+
+## Adapters
+
+- `mlx-adapter-v3/` — v3 training (rank 16, all layers, cosine LR)
+- `mlx-adapter/` — v2 training (rank 8, 16 layers)
+
+## Usage
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load(
+    "mlx-community/aya-expanse-8b-4bit",
+    adapter_path="nahommohan/tibeb-sft-adapter/mlx-adapter-v3"
+)
+
+prompt = "ስለ ቲ-ቢል ኢንቨስትመንት ንገረኝ"
+response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
+print(response)
+```
+
+## Links
+
+- Training pipeline: [github.com/nahomar/tibeb-training](https://github.com/nahomar/tibeb-training)
+- Dataset: [nahommohan/tibeb-training-data](https://huggingface.co/datasets/nahommohan/tibeb-training-data)