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gabai-models
jmptorralba/gabai-models
gabai-models is a machine learning model from jmptorralba. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
Trained model artifacts for GABai, an offline-capable AI reading tutor for Filipino learners aged 5–7. Code, docs, and setup instructions: https://github.com/jmptorralba/gabailocal
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Updated Jul 12, 2026
Repo size
9.8 MB
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.safetensors7.1 MB · 58%
At a glance
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Jul 12, 2026
- Updated
- Jul 12, 2026
- SHA
- b0157507
Base models
Model card
From the Hugging Face model README
GABai models — AI Reading Tutor (capstone deployment)
Trained model artifacts for GABai, an offline-capable AI reading tutor for Filipino learners aged 5–7. Code, docs, and setup instructions: https://github.com/jmptorralba/gabai_local
From the capstone project "Bridging Educational Gaps: A Scalable AI Model for Predicting Reading Proficiency and Delivering Adaptive Learning Experiences" (J.M.P. Torralba, University of Asia and the Pacific, MABA, 2025).
Contents
| File | What it is |
|---|---|
rf_pipeline.joblib | Random Forest reading-proficiency classifier (scikit-learn 1.6.1 — keep that version), trained on PISA Philippines 2018/2022 socio-demographic data. Predicts Critical / Not Critical reading proficiency from 9 background questions. ~66% accurate — the app treats it as a starting point with a mandatory human override. |
flan_t5_base_gabai_mcqa/ | LoRA adapter (r=16, q+v) for google/flan-t5-base, fine-tuned on FairytaleQA to generate a comprehension question + correct answer from a children's story. |
Not included here: the story-generation model. GABai uses the Q4_K_M GGUF of
Meta-Llama-3.1-8B-Instruct, downloaded from
QuantFactory/Meta-Llama-3.1-8B-Instruct-GGUF
(subject to the Llama 3.1 Community License). The GitHub repo's
download_models.py fetches everything, including this repo, and verifies
checksums.
Usage
Don't load these directly — they are wired into the GABai pipeline, which adds
the exact input encoding the classifier requires and a governance layer
(safety/readability/structure gates) around all generated content. Clone the
GitHub repo and run download_models.py.
License & data notes
- The QA adapter was fine-tuned on FairytaleQA (CC BY-NC 4.0) — this repo is therefore non-commercial (research and education use).
- The classifier was trained on publicly available OECD PISA data; it uses only anonymous background categories, no personal data.
- Base model
google/flan-t5-baseis Apache 2.0 (downloaded separately at first run).
- License
- cc-by-nc-4.0
- Languages
- en
- Created
- Jul 12, 2026
- Updated
- Jul 12, 2026