Sentence Similarity
sentence-transformers
context-curation
long-context
memory-management
conversational-ai
retrieval
llm
research-paper
Instructions to use SkyIsNotGreen/splinter-memory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SkyIsNotGreen/splinter-memory with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SkyIsNotGreen/splinter-memory") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Update the card to the Cascade-edition white paper
hive-devAdd the Cascade-style title/deck, opening, contents, one-page claim table and three headline results. Rename the sections, reorder the encoder subsections, add explicit limits/what-is-not-claimed and a separate Reproduce section, sync the canonical-paper and repository links, and correct the 35B verdict-set and 93.9% replay recall statements.970b4e6
19 days ago
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Splinter Memory: the white paper as the model card (long-context memory curation management)
Verified signature69ff84b