| 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. | |