Open-Weight Models

Detector weights and derived bases from a controlled study of occlusion compensation in aerial wildlife monitoring, along two axes: single view vs multi view, and sensor (pixel) space vs embedding space. container it ran in, every scored metric behind the paper's tables, and a step-by-step in REPLICATION.md. Run directory names here are the run identifiers used there, so a checkpoint joins directly against metrics/.json under the arms key. Backbones are not included. DINOv3 and V-JEPA 2.1 are used frozen and unmodified — obtain them from their original sources. Only self-trained artefacts are here. Nearly everything here is cross-validated. A run directory whose name carries f belongs to…

Models for the paper Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation (CoLLAs 2024) by Amir El-Ghoussani, Julia Hornauer, Gustavo Carneiro and Vasileios Belagiannis. Code: github.com/AmirMaEl/SemiSupMDE. A single U-Net (54.6M parameters) predicts depth from one RGB image. It is trained on synthetic Virtual KITTI with ground truth and adapted to real KITTI without any KITTI labels, using a pairwise loss on source images and a perturbation-consistency loss across RandAugment views of the unlabelled target images. Both checkpoints are PyTorch state dicts. KITTI Eigen test split (697 images), Garg crop, velodyne ground truth, no median scaling: With…

Surgically pruned 64-expert (~14.5B total) variant of Vortex5/Shadow-Siren-26B-A4B. - Sliced from 128 experts down to 64 experts across all MoE blocks. - Router gating matrix truncated cleanly to 64 rows. - Preserves original chat templates and tokenizer metadata for persistent multi-turn reasoning. - Full BF16 safetensors weights ready for fine-tuning or custom quantization.

Model

Shady_Fay_Krea_2_Turbo

AIxFun eStudio

Follow the license terms described in LICENSE.md.

static quants of https://huggingface.co/shreyansh12183/Shreyansh-STEM-AI-7B-Final For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some…

Model · Text classification

Sieve-9B-Plus

Sthānika AI

Sieve-9B-Plus is a decision model from the Sieve family. You give it a piece of text or JSON (the state) and typed questions, and it returns a calibrated probability for every option of every question. It reads the state once, scores the options directly, and never generates text. It is a LoRA adapter (rank 32) and a pointer head on Qwen/Qwen3.5-9B (revision c2022362), and it stores one calibration temperature per question category. The code is at - Backbone. Only the text part of Qwen3.5-9B is used; the model cannot produce text. - Readout. A pointer head scores the end of each option against the decision point. A calibrated softmax gives the probabilities. - Isolation. Each question runs…

Production FastAPI service for the Sigmo V2 motor-condition-monitoring system (MCSA — Motor Current Signature Analysis), carrying the ACTIVE classifier bundle v6.20260925.045900 (13-class fault taxonomy, accuracy 0.8995 / macro-F1 0.8475 held-out). - Docker (any builder): docker build. — the Dockerfile installs runtime dependencies, copies the code, and fetches the two large model artifacts (UBJSON booster + ISO gate) from this repository's pinned revision. current policy; appport: 7860). CORS is open () by default so external frontends (e.g. Replit) can fetch immediately; lock it down by setting CORSALLOWORIGINS. - DATABASEURL — Supabase PostgreSQL connection string. Without it the service…

This is Qwen3.8-27B that gets to the answer faster. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces 57% fewer answer tokens and uses 52% fewer thinking tokens, while matching or improving the measured answer quality of the base model. The percentages above were measured on the first release. The weights updated on 2026-09-13 trade a little of that reduction for stability; their re-measurement on the same prompt set is in progress and will replace these numbers. The result is substantially faster end-to-end generation: on typical chat prompts…

Model

SimpleMemVLN-R2R-RxR15deg-Window8-Text-DualLane-FromBase

Anh Dao

Completed two-epoch joint R2R + English-guide RxR 15-degree training run. epoch-1 contains update 3,852; epoch-2 contains the final update 7,704. Epoch 1 is a mid-schedule snapshot; epoch 2 completes the planned schedule. No navigation evaluation result is claimed for these weights. Original Qwen3.5-4B base revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. https://github.com/anhdao69/SimpleMemVLN/tree/streamingtextdual. 30,815 episodes; 3,128,624 action steps per dataset pass before distributed tail padding. Two epochs, 7,704 updates; 232 warmup updates; seed 429. Four H100 GPUs, one complete episode per rank per microstep, gradient accumulation 2, effective global batch 8. BF16, ZeRO-2…