Post
327
🧠 One Model to Classify, Verify, and Guard — Meet GLiClass-Instruct
When we first released GLiClass, it was a fast, zero-shot text classifier that could rival cross-encoders at a fraction of the cost. But classification alone wasn't enough. Our real ambition was a single, lightweight model that could handle the diverse range of text-understanding tasks via classification.
We are excited to announce GLiClass-Instruct – a leap forward that transforms GLiClass from a classifier into an instruction-following, multi-task engine.
What's new:
▪️ Hierarchical labeling: organize labels into structured groups for complex taxonomies
▪️ In-context learning via examples: provide few-shot examples to adapt on the fly, no fine-tuning needed
▪️ Prompting support: guide classification behavior with natural-language task descriptions
▪️ EWC for preventing catastrophic forgetting: add new capabilities without losing old ones
▪️ 3x faster inference thanks to FlashDeBERTa
New multi-task capabilities:
Beyond topic classification and sentiment analysis, GLiClass now supports:
▪️ Hallucination detection: verify whether LLM outputs are grounded in context
▪️ Rule-following verification: check if content complies with custom guidelines
▪️ Safety classification: detect prompt injections, jailbreaks, and harmful requests
These tasks are crucial for building reliable and efficient agentic systems, where every LLM output needs to be verified, every user input needs to be screened, and every response needs to follow the rules, all at minimal latency.
We release 3 instruction-following models (edge, base, large), with the large model matching SoTA classification models while unlocking entirely new task categories.
🔗 Explore more:
GitHub repo: https://github.com/Knowledgator/GLiClass
Models: https://huggingface.co/knowledgator/gliclass-multitask-large-v1.0
Our other solutions: https://www.knowledgator.com/
