Open-Weight Models Under gpl-3.0

Model · Tabular classification

DigitalEduTransformers

SnowFlash383935

Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох. Пример вывода: [False, True]

Model · Text to speech

sanoTTS

Ampixa Labs

sano (सानो) — Nepali for "small." A family of tiny neural text-to-speech voices — 294k to 2.27M parameters — that run with LM386 and a speaker), or live in the browser via WASM. every voice synthesizes your text live in the browser, no server, no upload. Both packages stream their weights from this repo by default. Python needs sanotts >= 0.3.0, the browser sanotts-web >= 0.3.0. Both fall back to the GitHub releases or the Pages host if Hugging Face cannot be reached, so an outage here cannot break an install. Python packages land in ~/.cache/sanotts/; set SANOTTSVOICESOURCE=hf or =github to pin one host. In the browser, passing voiceBase yourself turns the fallback off, so a self-hosted…

Weights for chessard, a network that predicts the move a human of a given rating would play. It is built on a Leela Chess Zero BT4 transformer body with a Stockfish-aware policy head. The engine and inference code live in the chessard-uci repository; this repo holds only weights. loras/players.json maps each adapter to the player's name and rating; the engine plays a player at that rating unless told otherwise. The adapters are rank-1 LoRA finetunes (float16, ~450 KB each) on that player's games. They only work on top of chessard.pt. Download the whole repo into one folder, then point the engine at it: Instead of --weights-dir you can set CHESSARDDIR=~/chessard-weights once. Every program…

Model · Object detection

CytoSPM

Smitih

Model weights of CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank (BIBM 2026). This is the model reported in the paper (Table III). The numbers above were measured with the released code on an RTX 3090; the paper values (33.55 / 52.20 / 38.17 and 15.00 / 21.00 / 17.20) were measured on an RTX 4090. Base AP is the COCO AP over the 24 base categories of PentaCyto; novel AP is the zero-shot COCO AP over the 9 held-out categories. The file contains model weights only (statedict) and loads with torch.load(..., weightsonly=True). See the code repository for installation, data preparation and training. The PentaCyto images and annotations are not included.

Model

elastic-wave-structure-emerge

Ziye Yu

Code and model release for “Reusable arrival information emerges across seismic models despite domain-dependent geometry.” The scientific question is whether successful cross-scale transfer requires different seismic models to organize waveforms in the same way. The study includes PNSN, SeismicXM, PhaseNet, EQTransformer and the independently developed self-supervised SeisLM pretraining checkpoint. Parameter scaling is context, not the axis of comparison. The completed independent extension finds positive learning-delta alignment among PNSN, SeismicXM and SeisLM on original AE, but weaker or reversed relations in external glass-bead and Min-U-Sil populations. Intervention directions also…

本仓库提供由 milesial/Pytorch-UNet 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-Pytorch-UNet - 模型显示名称：RK182X-CNN-Pytorch-UNet - 源模型：milesial/Pytorch-UNet - 模型类型：CNN 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：GNU General Public License v3.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。

本仓库提供由 meituan/YOLOv6 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-YOLOv6s - 模型显示名称：RK182X-CNN-YOLOv6s - 源模型：meituan/YOLOv6 - 模型类型：CNN 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：GNU General Public License v3.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。

This repository provides RKNN deployment configurations of yolov5m-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/yolov5. The upstream license is GNU GPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.

This repository provides RKNN deployment configurations of yolov5n-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/yolov5. The upstream license is GNU GPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.

This repository provides RKNN deployment configurations of yolov5s-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/yolov5. The upstream license is GNU GPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.

MCCbena's YOLOv7-tiny, fine-tuned for 35 classes using the local open-images-4s-yolo dataset. This is a modified/fine-tuned release, not an official upstream release. Training continues until all 300 epochs complete; the final checkpoint is published automatically after completion. This page is also updated with interim checkpoints during training. Recommended for inference: best.pt, validation epoch 128, [email protected] 54.47%, [email protected]:0.95 40.55%. It is selected by the upstream fitness score; last.pt remains the latest resumable checkpoint. When available, DATASETEXPANSION.json records a supplemental Hammer/Drill subset from the original Zenodo publisher, CC BY 4.0. SUPPLEMENTALDATASETLICENSE.md…