Daecore

Model · Text ranking

ettin-150m-memory-reranker-ft-v1

Daecore

An English cross-encoder for ranking passages from notes, procedures, decisions and technical documentation. Give it a query and candidate passages; it returns one relevance score per pair. It is a fine-tune of Ettin Reranker 150M and runs locally through ONNX Runtime. This revision adds a Vulkan FP32 graph, model.vulkan.onnx; the learned weights and the CUDA FP16 graph are unchanged. It omits the DirectML graph, which remains in the previous revision. Install tokenizers, numpy and onnxruntime-gpu[cuda,cudnn]==1.24.4. Keep model.onnx, model.onnx.data and the tokenizer from the same package together. This CUDA example assumes that package is in downloaded-model and scores two passages…

Model · Text classification

gliclass-std-base-v3-5facet-qint8-v2

Daecore

A GLiClass Base v3 fine-tune that describes what an English passage contains: a trap, decision, constraint, mechanism or procedure. A passage can have several facets. The model produces all five scores in one pass and runs locally through ONNX Runtime. This revision's ONNX graph adds Vulkan compatibility. Learned weights, prompts, calibration and thresholds are unchanged. On 1,300 generated passages from three document families excluded from training, macro average precision is 0.9676 for the fine-tune, 0.9384 for a TF-IDF baseline trained on the same rows, and 0.7709 for the zero-shot upstream checkpoint. Install onnxruntime==1.24.4, tokenizers and numpy. This CPU example assumes the…

Model · Sentence similarity

embeddinggemma-300m-memory-ft-v2

Daecore

An English embedding model for searching notes, procedures, decisions and technical documentation. It maps each query or passage to a normalized 768-dimensional vector and runs locally through ONNX Runtime. Fine-tuning starts from Google's EmbeddingGemma-300M. Version 2 succeeds the first Daecore fine-tune with new weights. Embed queries and passages with the same revision; re-embed stored passages when switching to v2. Install onnxruntime==1.24.4, tokenizers and numpy. Keep model.onnx, model.onnx.data and the tokenizer from the same package together. This CPU example assumes that package is in downloaded-model: Higher cosine similarity means a closer match. Use the query and passage…