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reacted to gianlucamazza's post with 🔥 1 day ago
I released my own benchmark sets behind emotional-memory, my library for mood-aware recall in LLM agents. The card opens with the limits: the main set is built to favour the method, and the negative results are listed up front, e.g. on LoCoMo AFT scores F1 0.168 against 0.271 for a naive RAG baseline. Use it to check where affect-conditioned retrieval helps and where it doesn't.
https://huggingface.co/datasets/gianlucamazza/emotional-memory-benchmarks reacted to gianlucamazza's post with 🚀 1 day ago
Upstream models my open-source projects run or my articles discuss. Each note says which it is. thenlper/gte-small is real use: RAGFS embeds file chunks with it locally. The two retrieval models from my RAG article, a cross-encoder re-ranker and a bi-encoder, are examples, labelled as such, not claims of production use.
https://huggingface.co/collections/gianlucamazza/models-used-in-my-projects-6aca0b3201f38f4c264ac9bf updated a model 4 days ago
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