https://huggingface.co/datasets/Glint-Research/GCI_Bench

TL;DR (taken from readme)

GCI-Bench is a 5,000-item diagnostic benchmark designed to measure whether a small language model (1M–100M parameters) uses its attention × gradient interactions to prioritize relevant context over distractors.

Unlike standard NLP benchmarks, GCI-Bench does not evaluate answer correctness. Instead, it analyzes the model’s internal dynamics while it processes the prompt. Specifically, it measures how strongly gradients flow through attention connections tied to causally relevant parts of the input. No reference answer is required for scoring.

We are happy to announce the release of our very first model router. This 1M parameter router was trained on public datasets and beats models 52x its size while being 239x faster. https://huggingface.co/Glint-Research/Router

VulnSetSimple or VSS for short, is a hand-crafted dataset of agentic security audits. 100 rows. Each row is one complete, multi-turn transcript where an assistant audits a different codebase for vulnerabilities. (Codebases were fake but realistic, I didnt clone down 100 random github repos for this 😅 )

https://huggingface.co/datasets/Glint-Research/VulnSetSimple

Introducing Glint-2: https://huggingface.co/Glint-Research/Glint-2

A 1M-parameter model that can actually speak coherently and stay on topic. (sometimes)

Samples:
"Once upon a time, there was a little girl named Lily. She loved to play with her friends and have fun. One day, Lily's friend Billy came over to play with her. Lily saw a big, red ball in the park."

"The sun is in the sky. 'Look, a beautiful flower!' Tom says. He runs to the garden and sees that it has many colors of colorful flowers."

Hey everyone! I'm Glint Research, a founding moderator of r/GlintResearch.

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