Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
SAVRN Model Hub
Model Collections
A collection is a selection with an argument attached: why these entries belong together, who put them together, and when it was last revised. None of them is a ranking.
Collections are short, curated lists that answer a practical question, such as which open text models are worth knowing or which models fit on a single accelerator. Each one states why its members were chosen, and none of them is a ranking.
We keep collections small on purpose. A long list shifts the work back to you; a short one with a clear reason saves time. Use them as starting points, then open each model's page for the license, the memory it needs and what it costs to run.
All collections
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.