DipaMed-1

A language model specialised on Nigerian clinical guidelines.

DipaMed-1 adapts Meta's Llama-3.1-8B to Nigerian medicine, grounded in Federal Ministry of Health (FMOH) and Nigeria Centre for Disease Control (NCDC) clinical guidelines. It is built to give locally-appropriate clinical guidance that reflects Nigerian disease priorities, the Nigerian Essential Medicines List, and national treatment protocols.

• Developed by: Destiny Ebhodaghe Ibhate (DipaHealth)
• Base model: meta-llama/Llama-3.1-8B
• Language: English
• License: Llama 3.1 Community License

Intended use: clinical decision support for trained health workers. DipaMed-1 is not an autonomous diagnostic system and must not be used to make patient-care decisions without a qualified clinician.

Highlights - where DipaMed-1 leads

On NigeriaMedQA, DipaMed-1 outperforms its base model on the Nigeria-specific clinical topics it was built for:

Topic	DipaMed-1	Base Llama-3.1-8B	Improvement
Mental health	95.8%	87.5%	+8.3
Maternal emergencies	90.6%	84.4%	+6.2
Drug availability	72.8%	67.0%	+5.8
Hypertension	68.5%	64.8%	+3.7
Tuberculosis-HIV	82.4%	79.4%	+3.0
Lassa fever	79.5%	76.9%	+2.6
Sickle cell disease	81.8%	80.0%	+1.8
Outbreak diseases	62.1%	60.3%	+1.8

These are the diseases and decisions that matter most in Nigerian practice. Across the full benchmark, DipaMed-1 performs comparably to the base model overall (76.3% vs 77.0%), while delivering these gains where Nigerian specialisation counts.

What makes it different

General medical models are trained on North American and European data. DipaMed-1 is grounded in Nigerian guidelines, giving Nigeria-appropriate answers a general model cannot: correct local first-line treatments, Essential-Medicines-List-aware choices, and NCDC/FMOH-aligned protocols.

How it was built

Stage	Purpose	Data
Continued pretraining	Absorb Nigerian medical knowledge	156 million words of Nigerian biomedical text (PubMed abstracts, open-access PMC full-text, clinical guidelines); approx. 254M tokens
Instruction tuning	Learn to answer clinical questions	Q&A generated from and independently verified against real Nigerian guidelines, plus cleaned expert-created sources (PubMedQA, MedQA-USMLE, WikiDoc)

Training used QLoRA (rank 16), a learning-rate sweep with model selection on a held-out validation set, and completion-only loss masking. The evaluation benchmark was kept fully uncontaminated and used only once for final scoring. The pretraining corpus is not released; its construction methodology is described in the accompanying paper.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DipaHealth/DipaMed-1"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
tok = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are DipaMed-1, a clinical AI assistant grounded in Nigerian guidelines."},
    {"role": "user", "content": "First-line treatment for uncomplicated malaria in a non-pregnant adult in Nigeria?"},
]
input_ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(input_ids=input_ids, max_new_tokens=256, do_sample=False, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))

Recommended deployment: retrieve, then answer (RAG)

For production, wrap DipaMed-1 in a retrieval-augmented (RAG) pipeline over the Nigerian guidelines: retrieve the relevant passage, give it to the model, and have it answer from that passage with a citation. This substantially improves factual reliability, especially for exact drug doses where all language models are unreliable from memory, and lets the system cite its source. A dedicated DipaMed RAG service is planned as a separate release.

Limitations and responsible use

• Exact doses: do not rely on DipaMed-1 for precise dosing without retrieval support; language models do not reliably memorise numeric dose tables.
• Decision support only: it assists clinicians and must not make autonomous clinical decisions.
• Scale: at 8B parameters it will not match frontier models on general medicine; its strength is Nigerian domain specialisation.
• Errors and bias: like all language models it can produce confident but incorrect answers; verify outputs against source guidelines.
• Scope: English, text-only in this version. Speech and Nigerian-language support are planned.

Citation

@misc{dipamed2026,
  title  = {DipaMed-1: A Nigerian Guideline-Specialised Clinical Language Model},
  author = {Ibhate, Destiny Ebhodaghe},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/DipaHealth/DipaMed-1}}
}

Acknowledgements

Built on Meta Llama-3.1-8B. Evaluated with NigeriaMedQA. Grounded in FMOH and NCDC clinical guidelines.