FastEdit focuses on rapid “model editing,” letting you surgically update facts or behaviors in an LLM without full fine-tuning. It implements practical editing algorithms that insert or revise knowledge with targeted parameter updates, aiming to preserve model quality outside the edited scope. This approach is valuable when you need urgent corrections—think product names, APIs, or fast-changing facts—without retraining on large corpora. The repository provides evaluation harnesses so you can measure locality (does the change stay contained?) and generalization (does the change apply where it should?). It’s structured for repeatable experiments, making side-by-side comparisons of editing methods and hyperparameters straightforward. For applied teams, FastEdit offers a toolbox to keep models current and compliant while minimizing collateral damage to overall performance.
Features
- Implementations of popular LLM editing algorithms for targeted updates
- CLI and Python APIs to perform single or batch edits quickly
- Evaluation utilities for locality, generalization, and robustness checks
- Integrations with common Transformer backbones and tokenizers
- Reproducible experiment setup for comparing methods and settings
- Artifacts to inspect before-and-after behavior across controlled probes