---
language:
- yo
- ha
- en
license: llama4
base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
tags:
- agriculture
- nigerian-languages
- yoruba
- hausa
- lora
- peft
- multilingual
- autoscientist
- together-ai
datasets:
- Professor/agronomy-qa-pairs
- Professor/naija-agri-dataset-yoruba-hausa
---

# Naija Agri LLM — Yoruba & Hausa Agricultural Assistant

A LoRA adapter for [Llama 4 Scout 17B 16E Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) (109B total parameters, MoE) fine-tuned on Nigerian agricultural Q&A data in Yoruba and Hausa using [AutoScientist by Adaption Labs](https://adaption.ai).

Built for the **Adaption Labs AutoScientist Challenge** (Agriculture category, 2026) — powering a crosslingual RAG-based agricultural assistant for Nigerian smallholder farmers.

## What It Does

Nigerian farmers and agricultural extension workers can ask questions in **Yoruba**, **Hausa**, or **English** and receive accurate, actionable answers grounded in Nigerian farming context — referencing institutions like IITA, NAERLS, and state ADP offices, and Nigerian crop varieties (cassava, yam, maize, cowpea, cocoa, plantain, tomatoes).

This model is the generative backbone of a RAG pipeline that supports crosslingual retrieval: a farmer's question in Yoruba retrieves relevant document chunks, which are passed as context to this model for answer generation in the farmer's language.

## Training

| Parameter | Value |
|-----------|-------|
| Base model | `meta-llama/Llama-4-Scout-17B-16E-Instruct` (109B total params) |
| Training infrastructure | Together AI (via AutoScientist) |
| Training method | SFT + LoRA |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.0 |
| Target modules | All linear layers |
| Epochs | 2 |
| Batch size | Max |
| Learning rate | 2e-4 (cosine decay, min ratio 0.1) |
| Warmup ratio | 0.05 |
| Max grad norm | 1 |
| Evaluations | 5 checkpoints |

### Training Metrics

| Metric | Value |
|--------|-------|
| Starting train loss | ~1.93 |
| Final train loss | ~0.94 |
| AutoScientist Win Rate (adapted) | **64%** |
| AutoScientist Win Rate (base) | 36% |

The adapted model wins 64 out of 100 head-to-head comparisons against the base Llama 4 Scout on agricultural Q&A in Yoruba and Hausa, per AutoScientist's internal preference evaluation. Train and validation loss converge smoothly with no signs of overfitting across 94 steps.

## Dataset

Trained on [`Professor/agronomy-qa-pairs`](https://huggingface.co/datasets/Professor/agronomy-qa-pairs) (~35,900 rows), localized from [KisanVaani/agriculture-qa-english-only](https://huggingface.co/datasets/KisanVaani/agriculture-qa-english-only) using Adaption Labs Data Adaption with a Nigerian context blueprint (IITA, NAERLS, ADP).

**Language distribution:**
- Yoruba (`yo`): ~30,900 rows
- Hausa (`ha`): ~4,600 rows
- Nigerian English (`en`): ~360 rows

## Intended Use

- Yoruba and Hausa speaking smallholder farmers in Nigeria
- Agricultural extension workers providing guidance in local languages
- RAG pipelines for crosslingual agricultural document retrieval
- Research on multilingual LLM adaptation for low-resource African languages

## Languages

This model responds in **Yoruba** (with full tone marks: ọ, ẹ, ṣ, etc.), **Hausa** (standard orthography), and **English**. The English capability comes from both the base model and Nigeria-specific English training rows, so English responses reflect Nigerian agricultural context rather than generic advice. It deflects out-of-scope questions in the user's language.

## Deployment

This adapter was trained on Together AI infrastructure. The trained model name on Together AI is `adaption_agronomy_qa_pairs`. It is compatible with Together AI's fine-tuned model serving — the base Llama 4 Scout 17B is hosted on Together AI and can be served with this LoRA.

## Limitations

- Fine-tuned on agriculture domain only; will deflect general queries
- Hausa coverage is smaller (~13% of training data) — may be less robust than Yoruba
- Quality of Yoruba tone marks depends on the localization pipeline used

## License

Built on Llama 4 — subject to [Meta's Llama 4 Community License Agreement](https://llama.com/llama4/license).