codelion/Qwen2.5-Coder-0.5B-Instruct-security-preference
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Asankhaya Sharma
codelion
AI & ML interests
Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and PTS. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.
Recent Activity
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5 days ago
Released 17 production-ready adaptive text classifiers that learn from just 100 examples per class and continuously improve without retraining.
These models achieve 93% average accuracy across enterprise use cases like email routing, fraud detection, document classification, and support ticket categorization. Built on ModernBERT with prototype memory and elastic weight consolidation.
Key benefits: 90% cost reduction vs API solutions, 90-120ms local inference, dynamic class addition, and zero vendor lock-in.
All models available under adaptive-classifier organization. Install with pip install adaptive-classifier.
Full technical details: https://huggingface.co/blog/codelion/enterprise-ready-classifiers
Code: https://github.com/codelion/adaptive-classifier
reacted
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their
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with ❤️
5 days ago
Released 17 production-ready adaptive text classifiers that learn from just 100 examples per class and continuously improve without retraining.
These models achieve 93% average accuracy across enterprise use cases like email routing, fraud detection, document classification, and support ticket categorization. Built on ModernBERT with prototype memory and elastic weight consolidation.
Key benefits: 90% cost reduction vs API solutions, 90-120ms local inference, dynamic class addition, and zero vendor lock-in.
All models available under adaptive-classifier organization. Install with pip install adaptive-classifier.
Full technical details: https://huggingface.co/blog/codelion/enterprise-ready-classifiers
Code: https://github.com/codelion/adaptive-classifier
reacted
to
their
post
with 👀
5 days ago
Released 17 production-ready adaptive text classifiers that learn from just 100 examples per class and continuously improve without retraining.
These models achieve 93% average accuracy across enterprise use cases like email routing, fraud detection, document classification, and support ticket categorization. Built on ModernBERT with prototype memory and elastic weight consolidation.
Key benefits: 90% cost reduction vs API solutions, 90-120ms local inference, dynamic class addition, and zero vendor lock-in.
All models available under adaptive-classifier organization. Install with pip install adaptive-classifier.
Full technical details: https://huggingface.co/blog/codelion/enterprise-ready-classifiers
Code: https://github.com/codelion/adaptive-classifier
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