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Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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LLMs1 min read
Hugging Face released a new training pipeline for multi-vector encoder models using Sentence Transformers. This allows for efficient training and finetuning of these models for various downstream tasks.
From Hugging Face blog
LLMs1 min read
Sentence Transformers has released new multi-vector embedding models designed for late interaction. These models offer improved performance for tasks requiring understanding of context and relationships between multiple pieces of information.
From Hugging Face blog
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
