📈 New blog post: Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers. As a practical example, I finetuned a ColBERT-style model for medical retrieval. 14.5 hours on one RTX 3090, and it beats every general-purpose retriever I could find. Thread 🧵
This is by far the cutest robot I've ever seen. Under $400, with a camera, speaker, LiDAR, NFC, Bluetooth, Wifi, etc. + you can train it yourself with reinforcement learning. Plus it has rollerskates. It's so precious 🦆
Tencent just published R3-Skill, a two-stage retrieval stack purpose-built for a problem RAG-style retrievers weren't designed for: routing LLM agent skills (think Anthropic's SKILLmd format). Two 0.6B models, both Apache 2.0, one embedding model, and one reranker. 🧵
🤗 I've just released SetFit v1.2.0! SetFit trains text classifiers from a handful of labeled examples per class by fine-tuning a Sentence Transformer, no prompts or LLMs needed. v1.2 brings support for transformers v5, Sentence Transformers v6 & huggingface_hub v1. Thread 🧵
🎉 @lightonai.bsky.social just published LightOn-rerank: rerankers that score text passages or document page images against a query. Six models: Qwen3.5 at 0.8B / 2B / 4B, each in a pointwise and a generative listwise variant. Excellent for text <-> image retrieval. 🧵
💧 Liquid AI released 2 multilingual retrieval models, the first bidirectional members of the LFM family. Both 350M params, 11 languages (ar, de, en, es, fr, it, ja, ko, no, pt, sv): - LFM2.5-Embedding-350M (bi-encoder) - LFM2.5-ColBERT-350M (multi-vector, late interaction) 🧵
