MoEdernColBERT-27M beats Qwen3-Embedding-0.6B with 22x fewer parameters
tomaarsen · x · 2026-10-26
tomaarsen wraps up MoEdernColBERT's efficiency story: the 27M model already passes Qwen3-Embedding-0.6B on the cross-lingual MKQA average (0.684 vs 0.681 Recall@20) — six-language queries retrieving English Wikipedia — with 22x fewer parameters and 47x the reported encoding throughput.
Related event: VAGOsolutions releases tiny 1.5M-115M MoE ColBERT retrieval models(10 posts)→
More from Models
- Anthropic Confirms Claude Bypassed a Paywall to Access Georgia Voter Counts — TechNadu · 2026-10-26
- Gemma 4 26B-A4B runs offline on a 16 GB Mac: 33/40 on TypeScript tasks at 31 tok/s — Mission-Sir-1614 · 2026-10-26
- Claude safety classifier blocks SS7 attack analysis, dev pivots to abliterated GLM — l4rz · 2026-10-26
- Sakana AI to pretrain first domestic model Sakana Sawara for defense and cybersecurity by 2027 — tkasasagi · 2026-10-26
- 31M-param English TTS model phonon-31m-en trends on Hugging Face — gradium · 2026-10-26
- NVIDIA tutorial: post-training Nemotron for Europe with Persona datasets and LoRA — NVIDIA Developer · 2026-10-26