A single LoRA adapter for answering maternal, sexual and reproductive-health questions written in English and several African languages (Swahili, Luganda, Amharic, Akan).

Model

Name: Qwen3.5-27B
Uses LoRA
Effective batch size: 64

This repository contains the LoRA adapter only. To run inference you must download the base model Qwen/Qwen3.5-27B separately and attach this adapter to it — the adapter on its own cannot generate.

Approach

Every question is answered in context: a few nearest same-language question/answer precedents are retrieved from a reference pool and placed in front of the question, and the model is asked to answer by leaning on them. Training and inference use the exact same layout, so one adapter is enough.

Intended use

Generating grounded answers to health questions in the supported languages, for research and benchmark evaluation. Given a question and its retrieved precedents, the model returns a single answer in the question's language.

Limitations

Not medical advice. Outputs can be wrong or incomplete and must never guide real clinical, diagnostic or treatment decisions.
Quality is uneven across languages — stronger on higher-resource subsets, weaker on the lowest-resource ones.
The answer depends on the retrieved precedents; weak retrieval degrades it.
Greedy decoding only, no calibrated confidence, no safety filtering.

Requirements

1× 80 GB GPU for inference
Python packages: vllm, sentence-transformers, pandas, numpy, huggingface_hub
A sentence-embedding retriever (McGill-NLP/AfriE5-Large-instruct) to build the precedent block
The base backbone Qwen/Qwen3.5-27B

Run inference

Open inference.ipynb and run it top to bottom. It will:

Fetch this adapter and the retriever, and load the separately-downloaded backbone.
Build the precedent index from the reference pool.
For each question, retrieve the top precedents, render the prompt, and generate greedily.
Write submission.csv.

Set the environment variable LIMIT=16 first for a quick smoke test, or leave it unset to score the whole test set. Point BASE_MODEL / ADAPTER_REPO at local folders if you already have them.

Reproduce & verify

inference.ipynb regenerates submission.csv and runs structural checks (columns, target consistency, id ordering, no empty rows). The reported score is verified by uploading that file to the benchmark.

A note on variance. vLLM strongly affects both fine-tuning and inference — re-running the same recipe through vLLM can move the score by up to ±0.004 on both public and private. This has been observed repeatedly across many runs, so the reported number should be read as a point within that band, not an exact target.

rayserve/qwen35-27b-afri-health-qa-lora

Health-QA adapter (Qwen3.5-27B + LoRA)