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The training service kept only the latest loss, so a live loss chart could show a single point. Fold each progress event into bounded (step, loss, lr) history arrays (capped at 4000 points, decimated when full) plus the latest throughput and peak VRAM, and record the family / base model / catalog path on completion. The status endpoint returns these as a nested metric_history object the UI can chart directly, and the start request accepts an optional model_family override. |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||