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NabajyotiPathak/kiswahili-ai-blended

Kiswahili AI — Swahili Instruction Model AutoScientist Challenge 2026 — Language Category Overview Kiswahili AI is a Swahili instruction-tuned language model fine-tuned from Llama-4-Scout-17B-16E-Instruct (109B MoE) using AutoScientist by Adaption Labs. It combines 4 public Swahili datasets into a unified instruction dataset (~52K rows), processes them through Adaptive Data for quality enhancement, and trains via AutoScientist's closed-loop co-optimization.… See the full description on the dataset page: https://huggingface.co/datasets/NabajyotiPathak/kiswahili-ai-blended.

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Dataset Card

Kiswahili AI — Swahili Instruction Model

AutoScientist Challenge 2026 — Language Category

Overview

Kiswahili AI is a Swahili instruction-tuned language model fine-tuned from Llama-4-Scout-17B-16E-Instruct (109B MoE) using AutoScientist by Adaption Labs. It combines 4 public Swahili datasets into a unified instruction dataset (~52K rows), processes them through Adaptive Data for quality enhancement, and trains via AutoScientist's closed-loop co-optimization.

Result: 73% win rate (adapted) vs 27% (baseline) — +170% relative improvement on Adaption's held-out test set.

Why Swahili? Over 100 million speakers across East Africa. Swahili is a low-resource language where current LLMs show a 28-45% performance gap compared to English.

Dataset

Source	Rows	Type
FineTome-20k-sw	17,982	General instruction
KenSwQuAD	7,506	Extractive QA
Code-170k-swahili	14,969	Code conversations
Swahili-Corpus-Dataset	12,267	Raw text (converted to instruction)
Total	52,118 (after dedup)

Data Adaptation

The raw dataset scored Grade D (6.9th percentile). After Adaptive Data processing (deduplication + reasoning traces + hallucination mitigation), quality improved +62% to Grade B (25.6th percentile).

Training

Setting	Value
Base Model	meta-llama/Llama-4-Scout-17B-16E-Instruct (109B MoE)
Method	SFT with LoRA (r=64, alpha=128, all-linear)
Epochs	1
Batch Size	max
Learning Rate	0.0001 (cosine scheduler)
Warmup Ratio	0.03
Weight Decay	0.02
Platform	AutoScientist by Adaption Labs

Results

Metric	Baseline	Adapted	Improvement
Win Rate	27%	73%	+170%
General Category Win Rate	31%	69%	+123%

Pipeline

Public HF Datasets → Blend Script → JSONL → Adaptive Data → 
Adaptation (D→B) → AutoScientist → Trained Model → HF + Kaggle

1.Blend: data/blend_swahili.py downloads and merges 4 datasets
2.Validate: data/validate_format.py checks JSONL quality
3.Adapt: Adaptive Data — deduplication, reasoning traces, hallucination mitigation
4.Train: AutoScientist co-optimizes data + training recipe
5.Release: Weights + dataset on Hugging Face + Kaggle

How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("NabajyotiPathak/kiswahili-ai")
tokenizer = AutoTokenizer.from_pretrained("NabajyotiPathak/kiswahili-ai")

prompt = "Elezea umuhimu wa teknolojia katika elimu."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0]))

Project Structure

swahili-ai/
├── data/
│   ├── blend_swahili.py       # Download + blend 4 datasets
│   ├── validate_format.py     # Validate output format
│   ├── sources.json           # Dataset provenance
│   └── swahili_blended/       # Output directory (generated)
├── docs/
│   ├── MODEL_CARD.md          # Hugging Face model card
│   └── DATASET_CARD.md        # Hugging Face dataset card
├── adaptive_data_config.md    # Adaptive Data settings
├── autoscientist_config.md    # AutoScientist experiment config
└── README.md

Reproducibility

1.Run data/blend_swahili.py to recreate the blended dataset from public sources
2.Upload to Adaptive Data with: deduplication + reasoning traces + hallucination mitigation
3.Train via AutoScientist with LoRA on Llama-4-Scout-17B

Judging Criteria Alignment

Criterion	How We Address It
Performance improvement	27% → 73% win rate (+170%) on held-out test set
Dataset originality	Multi-source blend (4 datasets) + Adaptive Data enhancement
Real-world impact	100M+ Swahili speakers underserved by current AI
AutoScientist depth	Full co-optimization loop: data adaptation → training → convergence
Release quality	Model card, dataset card, reproducible pipeline, open weights

License

Apache 2.0

Acknowledgments

—Adaption Labs — AutoScientist + Adaptive Data + free compute
—Samwel Ngusa — FineTome-20k-sw
—Kencorpus Project — KenSwQuAD
—Mich Seth Owusu — Code-170k-swahili
—Noel Masasi & Bernard Masua — Swahili-Corpus-Dataset