My public work on the Hub: the emotional-memory demo and benchmark dataset, the mklang playground, the affective-fly demo, and an int4 ONNX Runtime GenAI copy of SmolLM2-360M-Instruct. Each card states what it is for and where it stops working. The SmolLM2 copy is experimental, with no quality evaluation. https://huggingface.co/collections/gianlucamazza/agent-memory-affect-mklang-and-local-inference
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. gianlucamazza/models-used-in-my-projects-6aca0b3201f38f4c264ac9bf
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. gianlucamazza/emotional-memory-benchmarks
