vekarika/farmhealth-ai

Offline Agricultural & Livestock Health Advisor for African Farmers and Extension Officers # FarmHealth AI — ADTC 2026 Laptop LLM Submission (Agriculture) **Offline agricultural & livestock-health advisor for farmers and extension officers in northern Nigeria** (Nasarawa, Kaduna, Adamawa, Kwali) — runs entirely on an 8 GB laptop with zero connectivity, in **English and Hausa**, through llama.cpp on a quantized GGUF model. Built to the official ADTC 2026 submission template, and organised around how the challenge **actually scores** submissions. --- ## How scoring drives the design The profiler runs the **bare GGUF model** (no app/RAG in the scored loop): ``` S_total = 0.50·S_acc + 0.30·S_perf + 0.20·S_eff − P_thermal (+ up to 10 African-use-case points) (lm-eval MCQ) (llama-bench) (peak RSS) (>85°C) ``` - **Accuracy (50%)** = multiple-choice (lm-eval `acc_norm`) on the bare model → won by a **fine-tuned small model** + a strong domain dataset. RAG is *not* in this loop. - **Speed (30%) + Efficiency (20%)** → reward a **small, lean** model (target ≥15 t/s, low RAM, <7 GB hard ceiling). - **Cross-disciplinary integration** = location-aware **offline RAG** (information retrieval) → the qualitative score, the demo, and the live defense. - **African use-case bonus** (≤10 pts) → English-primary model with **Hausa** support + genuine local grounding. Architecture principle: **the model reasons; retrieval supplies what changes by place and week.** --- ## Repo map ``` . ├── metadata.json # ADTC submission metadata (schema-valid; fill team_id + github_handle) ├── download_model.sh # pulls the public GGUF into model/ (edit URL after hosting weights) ├── REPORT.md # technical writeup (problem, design, constraints, benchmarks) ├── model/ # GGUF lands here at eval time (gitignored — never commit weights) │ ├── ADTC_MASTER_SPEC.md # authoritative rules/timeline/scoring (single source of truth) ├── DOMAIN_MAP.md # knowledge coverage map (stable→fine-tune vs volatile→RAG) ├── DATASET_SPEC.m …