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| Language | Country | Speakers |
|---|---|---|
| Hausa | Nigeria/Niger/Chad | 70M+ |
| Yoruba | Nigeria/Benin/Togo | 45M+ |
| Igbo | Nigeria | 30M+ |
| Nigerian Pidgin | West Africa | 75M+ |
| Swahili | Kenya/Tanzania | 200M+ |
| Amharic | Ethiopia | 35M+ |
| Somali | Somalia/Kenya | 20M+ |
| Xhosa | South Africa | 27M+ |
| Zulu | South Africa | 27M+ |
| Shona | Zimbabwe | 15M+ |
| Luganda | Uganda | 8M+ |
| Lingala | DRC/Congo | 70M+ |
| Wolof | Senegal/Gambia | 12M+ |
| Oromo | Ethiopia/Kenya | 40M+ |
| Parameter | Value |
|---|---|
| Base Model | Llama 3.2 3B Instruct |
| Training Pairs | 86,604 |
| Training Steps | 500 |
| Final Loss | 2.07 |
| Method | QLoRA (r=16) via Unsloth |
| Hardware | NVIDIA Tesla T4 |
| Data Sources | Masakhane, Glot500, CulturaX |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3tokenizer = AutoTokenizer.from_pretrained("okaforpascal40/BaobabAI-v0.2")
4model = AutoModelForCausalLM.from_pretrained("okaforpascal40/BaobabAI-v0.2")