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Added an image and updated the quick start section.
Introduce visual plots tracking cross-entropy loss over 6,000 steps and ~78 minutes of wall-clock training time. These curves serve as a baseline reference for the model's convergence behavior. The plots highlight a steady decline in training loss down to ~3.4, while validation loss plateaus early on, achieving its best score of 4.1319 at step 3900. Adding these assets to the repository ensures we have a clear, permanent record of this run's performance to compare against future optimization and hyperparameter tuning passes.
* Update README with image and quick start section Added an image and updated the quick start section. * doc: Add training and validation loss curves to assets (#35) * Update README with image and quick start section (#34) Added an image and updated the quick start section. * doc: Add training and validation loss curves to assets Introduce visual plots tracking cross-entropy loss over 6,000 steps and ~78 minutes of wall-clock training time. These curves serve as a baseline reference for the model's convergence behavior. The plots highlight a steady decline in training loss down to ~3.4, while validation loss plateaus early on, achieving its best score of 4.1319 at step 3900. Adding these assets to the repository ensures we have a clear, permanent record of this run's performance to compare against future optimization and hyperparameter tuning passes. * main: Configure GPT-style model hyperparameters and tokenizer (#36) * Update README with image and quick start section (#34) Added an image and updated the quick start section. * doc: Add training and validation loss curves to assets Introduce visual plots tracking cross-entropy loss over 6,000 steps and ~78 minutes of wall-clock training time. These curves serve as a baseline reference for the model's convergence behavior. The plots highlight a steady decline in training loss down to ~3.4, while validation loss plateaus early on, achieving its best score of 4.1319 at step 3900. Adding these assets to the repository ensures we have a clear, permanent record of this run's performance to compare against future optimization and hyperparameter tuning passes. * main: Config model hyperparameters and tokenizer * V1.3.15 (#37) * Update README with image and quick start section (#34) Added an image and updated the quick start section. * doc: Add training and validation loss curves to assets Introduce visual plots tracking cross-entropy loss over 6,000 steps and ~78 minutes of wall-clock training time. These curves serve as a baseline reference for the model's convergence behavior. The plots highlight a steady decline in training loss down to ~3.4, while validation loss plateaus early on, achieving its best score of 4.1319 at step 3900. Adding these assets to the repository ensures we have a clear, permanent record of this run's performance to compare against future optimization and hyperparameter tuning passes. * main: Config model hyperparameters and tokenizer * Delete quadtrix_training_report.png * Delete run.md
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Introduce the project documentation detailing setup, execution, and build
instructions for both the PyTorch and native C++ inference/training paths.
The documentation defines clear workflows for:
engine/main.py.it for training, terminal chats, or raw generation tasks.
Licensed under GPL 3.0.