Author: Hussein Adeiza (mabera)
Role: Licensed Environmental Health Officer, Abuja Nigeria
Base Model: Mixtral 8x7B
Fine-tuned with: AutoScientist by Adaption Labs
Model Description
A LoRA adapter fine-tuned to interpret real global tech layoffs data,
producing structured analytical reasoning grounded entirely in
directly-downloaded statistics from a real tracked dataset (layoffs.fyi).
This submission completes all 7 categories of the AutoScientist
Challenge 2026 Part 2.
A Note on Subject Matter
This dataset addresses real workforce reductions affecting real
people's livelihoods. All interpretation stays measured and analytical,
including honest disclosure of data limitations (missing figures,
likely reporting bias) rather than overstating confidence in the
underlying figures.
Training Data
Source: layoffs.fyi tracked data, accessed via a properly-cited
GitHub repository, downloaded directly, 2,361 real company entries
(2020-2023)
Dataset: 5 original prompt-completion pairs, every number computed
via pandas directly from the raw source file, expanded via Adaptive
Data (no recipe modifications, full 20K+ datapoint expansion)
Win rate (on dataset): 70% adapted vs 30% base model
Base model: mistralai/Mixtral-8x7B-Instruct-v0.1
Method: LoRA, no recipe modifications
Dataset quality: 8.0 → 8.6 (+7.5% relative improvement, Grade B)
Percentile: 31.5
Domain classification: Data-analysis-visualization (60%) / Market-
analysis (20%) / Corporate-business (20%), not a clean HR match;
disclosed here as this submission was intended for the HR category
Key Cited Findings (from the raw downloaded source only)
Global tracked layoffs followed a non-monotonic trend: 81,068 in
2020 (COVID shock), falling to 15,823 in 2021 (recovery), then
rising to 161,711 in 2022 and 127,277 in the first quarter of 2023
alone
Funding raised shows almost no correlation with layoff scale
(r = 0.077 across 1,491 companies with complete data), challenging
the intuitive assumption that better-funded companies cut more
Post-IPO (mature, publicly-traded) companies account for 53.1% of
total tracked layoffs, likely reflecting company scale rather than
instability
Roughly a third of entries (31.3% missing headcount, 33.2% missing
percentage) lack key figures, meaning aggregate totals are
conservative lower bounds, not precise counts
The dataset's heavy US concentration (66% of the geographic total)
most likely reflects reporting bias in the underlying tracker toward
English-language tech media, not a true global distribution
Credits
Powered by Adaptive Data — Adaption Labs
AutoScientist Challenge 2026, Part 2 — HR Category
