Blog
Notes on adapters, fine-tuning, and running models on your own hardware.
Adapter Metadata Fields That Matter
Which metadata fields belong on an adapter card and why they matter to users, downloads, and API clients.
2025-03-10LoRA Rank and Alpha Explained
How rank and alpha shape adapter capacity, file size, and stability in practice.
2025-03-05Using Synthetic Data to Bootstrap a LoRA
Generating training examples with a bigger model when the real dataset is too small.
2025-02-24Monitoring Fine-Tune Jobs in Practice
What to watch while a fine-tune job is running and which signals mean the run should be stopped or adjusted.
2025-02-17Auditing Dataset Quality Before Training
Cheap checks that catch dataset problems before a training run wastes hours.
2025-02-08Preparing a Dataset for Adapter Training
Practical dataset shape, cleaning steps, and formatting choices before you start training.
2025-01-29
Evaluating LoRA Adapters
A practical checklist for comparing adapter quality with held-out prompts, seeds, and regression tests.
2025-01-20
DoRA: When It Beats Vanilla LoRA
Weight-Decomposed Low-Rank Adaptation, and where it actually improves over LoRA.
2025-01-14Troubleshooting Overfit LoRA Adapters
How to spot and fix adapters that memorize the training data instead of generalizing.
2025-01-06Troubleshooting Weak LoRA Outputs
A step-by-step way to diagnose adapters that barely change the model output after training.
2024-12-16