A keyless, offline playground for mklang, my language and interpreter for LLM control flow. Parse, check and lint, format, and run the bundled examples and conformance cases. No model runs and no API key is needed; judge picks come from fixture files. gianlucamazza/mklang-playground
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
