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title: Open Model Training Lab
emoji: 🧪
colorFrom: blue
colorTo: yellow
sdk: static
app_file: index.html
pinned: false
license: mit
datasets:
- PolyAI/banking77
models:
- Qwen/Qwen3-1.7B-MLX-bf16
- google-bert/bert-large-cased
- microsoft/deberta-v3-large
tags:
- apple-silicon
- fine-tuning
- mlx
- pytorch
- education
---
# Open Model Training Lab
An interactive, beginner-friendly account of training Qwen3, BERT-Large and
DeBERTa-v3-large for BANKING77 intent classification on an Apple M2 Max.
The guide covers fifteen controlled experiments, including numerical failures,
rejected refinements, data-leakage protections, a searchable glossary, quiz and
interview practice.
**Best recorded result:** 92.99% validation accuracy and 94.12%
reporting-only test accuracy with a DeBERTa upper-layer refinement.
Source and reproduction instructions:
[msulemans/open-model-training-lab](https://github.com/msulemans/open-model-training-lab)
This is educational software, not a production banking classifier.
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