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
File size: 919 Bytes
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message: "If you use this software or its measurements, cite it as below."
title: "Splinter-Memory"
abstract: >-
An external, managed-decay context-curation layer for long-horizon LLM
conversations. Small CPU "drone" encoders score, filter, and reassemble
stored history into a bounded, high-relevance window per turn, so a
generative model performs over arbitrarily long conversations on consumer
hardware. Claims are stated as falsifiable predictions (P1-P11) with measured
verdicts and a published failure register.
type: software
authors:
- name: "sky-is-green"
alias: "sky-is-green"
version: "0.1.0"
date-released: "2026-09-22"
license: MIT
repository-code: "https://github.com/sky-is-green/splinter-memory"
url: "https://github.com/sky-is-green/splinter-memory"
keywords:
- context-management
- memory-management
- long-context
- retrieval
- llm
- local-inference
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