nb-asr-qwen06b-bokmal-nynorsk-reranker

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 ranking

rerank-indonesia-Q8_0-GGUF

Radhi Fadlillah

This model was converted to GGUF format from madebyaris/rerank-indonesia using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

本仓库提供由 Qwen/Qwen3-Reranker-0.6B 转换的 RKNN 模型。 - Model ID：RKNNAI/RK182X-EMBEDDING-Qwen3-Reranker-0.6B - 模型显示名称：RK182X-EMBEDDING-Qwen3-Reranker-0.6B - 源模型：Qwen/Qwen3-Reranker-0.6B - 模型类型：EMBEDDING ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属与转换修改说明见 NOTICE。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本包不包含运行库。

Model · Text ranking

CLM-v0.1-8B-gguf

EvoAwaken Workshop

This repository contains the state and action projection heads only from CLM scores candidate actions against a state; it does not generate text. A separate, frozen Qwen3-8B encoder is required to turn text into embeddings. The source checkpoint is CLMv0.1-8B.pt (SHA-256 b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5). The converter preserves the F32 weight and bias bytes. It stores the reference scoring scale min(exp(logitscale), 100) = 100 in the GGUF. The GGUF does not contain the Qwen3-8B encoder, tokenizer, or original.pt file. The verified encoder file is SHA-256 5e416a2020fe63e76ea13c8979be35fc6070aaf3578f7876400c55c2f5c3eb30. Use rust-model-inference at or after…

Why the disagreement is the feature, not the bug: kev downgrades high-frequency auto-recovering alerts (the #1 source of alert fatigue) and escalates orchestration-core outages that static rules treat as routine. - None-injection (p=0.1): teaches the head to abstain, not everything deserves a label We ran the obvious next experiment: fine-tune it on its own production data (5-fold grouped CV, 4-arm temperature calibration). Result: 0 gain over the deployed version — and we did not deploy it, per preset criteria defined before the experiment. That's the discipline this model is proud of: judge the upgrade on pre-registered criteria, not vibes. Training receipts and the arbitration table are…

This is Horizon-Labs/multilingual-reranker-small in a smaller ONNX file, for running on a CPU. Same model, same tokenizer, same scores to within a small margin. The file is about a third smaller than the original quantized one. We use it in Sno Station to rerank memories for coding agents. The token embeddings, the attention projections and the first MLP projection are stored in 8 bits. The second MLP projection stays in float. Its inputs carry a few very large values, and 8 bits move the scores too much there. Weights are unsigned 8-bit, per channel. Signed weights overflow on AVX2 CPUs without VNNI and flatten every score. quantize.py rebuilds the file from the original onnx/model.onnx at…