Fine-tuned Gemma E2B offline deployment with dashboard and conversational interfaces¶
Product or Programme · All products and programmes
Evidence · 3 claims (1 for, 2 mixed) · 2 studies (2 causal),q1–q2· 0 of 2 report an effect size · 3 claims rest on one study
Description¶
Chenguang Pan et al.'s offline educational-intervention-effect explanation system, combining a LoRA-adapted Gemma E2B model with dashboard and conversational interfaces for parents, students, counselors, and other stakeholders.
Components¶
Claims¶
- LoRA fine-tuning hyperparameters are not model-agnostic: increasing rank above 64 destroyed LLaMA's feedback generation while Gemma remained robust [~W]
- LLaMA's LoRA adaptation compresses its prediction range to roughly 2.0–4.0, while Gemma maintains a broader distribution closer to the ground truth [+W]
- Adaptation gains grow monotonically with model capacity under the same LoRA recipe, though three data points cannot establish a scaling law [~W]
Related Products and Programmes¶
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Key Sources¶
- Chenguang Pan, Airui Meng, and Youmi Suk. (2026). Distilling Black-Box Machine Learning into a Small, Self-Explaining Language Model for Learning Analytics. arXiv. https://arxiv.org/abs/2608.21165
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
fine-tuned-gemma-e2b-offline-deployment-with-dashboard-and-conversational-interfaces |
title |
Fine-tuned Gemma E2B offline deployment with dashboard and conversational interfaces |
description |
Chenguang Pan et al.'s offline educational-intervention-effect explanation system, combining a LoRA-adapted Gemma E2B model with dashboard and conversational interfaces for parents, students, counselors, and other stakeholders. |
status |
review |
product_kind |
software |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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