GIMKit
Guided Infilling Modeling Toolkit — structured text generation and information extraction using language models.
GIMKit lets you define placeholders (masked tags) in text and have a language model fill them in. It gives you fine-grained control over model outputs through a typed tag system with optional regex constraints.
What Can You Do With GIMKit?
GIMKit is a general-purpose information extraction framework. Write a natural-language template with embedded typed placeholders, and the model extracts structured data from any unstructured text.
| Use Case | Description |
|---|---|
| Contact extraction | Parse names, emails, phone numbers from free-form text |
| Named entity recognition | Extract organizations, people, locations, dates |
| Text classification | Categorize text into labels, assign sentiment |
| Event extraction | Pull structured event info (what/where/when/impact) |
| Relation extraction | Find entities and the relationships between them |
| Resume / CV parsing | Extract candidate name, title, education, experience |
| Product review analysis | Parse product, price, rating, pros and cons |
| Privacy & PII protection | Extract, classify, redact, and filter PII |
See the Classic IE Use Cases, Privacy and PII Use Cases, and Other Use Cases pages for full code examples.
Features
- Masked tag system — embed typed placeholders directly in f-strings.
- Regex constraints — restrict model output to specific patterns.
- Named access — retrieve results by tag name or index.
- Multiple backends — OpenAI, vLLM (server and offline).
- Small-model friendly — designed to work well with compact open-source models.