SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Adaptive Geometry-Aware Fourier Neural Operator — with the complete controlled-evidence stack, extended depth sweep to 16, a second PDE family, a deformation baseline, a direct measurement of geometric forgetting, and a fully programmatic research paper (paper/agfnopaper.pdf). (mode truncation discards everything above the cut). A zero-gated, SDF-derived multiplicative modulation of the spectral weights restores the truncated band by spectral convolution — and the paper measures the whole story: diagnosis (proposition), fix (mechanism), consequence (probe). = 0.971× FNO's global error — the gain is NOT extra parameters (5.10M vs 4.81M) or channels (identical 3-channel inputs). −56% ring.…
This is a three-class Japanese/English sentiment classifier for short hospitality reviews, fine-tuned from google-bert/bert-base-multilingual-cased. It categorizes each review as negative, neutral, or positive. The model predicts one overall label for a short Japanese or English hospitality It is intended for research, prototyping, and human-reviewed analytics. It must not be used as the sole basis for consequential business, employment, moderation, or customer-service decisions. Applications using this model should allow operators to review and correct its predictions. The model was fine-tuned on 480 synthetic bilingual reviews: 240 Japanese and 240 English, balanced across the three…
This repository contains the merged FP32 ONNX model used by @clueso/ai-text-detector, a local AI-text detector for Node.js. It is a LoRA fine-tune of wasitaigeneratedcom/ai-text-detector-small. The original four output classes and mask-aware mean-pooling architecture are preserved: The ONNX graph contains the DeBERTa encoder, mask-aware mean pooling, and four-class classifier. No remote code is required. Only the published MAGE training split was used for gradient updates. The accepted training sample contained 4,000 human and 4,000 AI documents, balanced across sources. Rank-8 LoRA adapters were trained on all 24 layers' query and value projections together with the classifier. This made…
Public, anonymous-download assets for the AI3D Local Modeler installer. Release manifests pin an immutable 40-character repository commit SHA as well as every file's exact byte size and SHA256. The Stable Fast 3D model and configuration are distributed under the Stability AI Community License. Keep STABILITYAICOMMUNITYLICENSE.md and STABILITYAINOTICE.txt with every redistribution. The runtime contains its own third-party notices. The reduced COLMAP archive contains exact source inputs, patches, build instructions, and dependency licenses; COLMAP itself is BSD-3-Clause.
Runs of the detector comparison in and the models quantized from them and the C code generated from those for the board. The format can change between commits, so read those pages at the commit in the directory's meta.json. The runs were fitted on 20260916-001002, 20260916-064753, 20260916-234726 and 20260919-025203 used its revision 4cc59c1.
Model cho worker ComfyUI (toitx/comfyui-video-runpod ≥ 1.6.0), endpoint "nhân vật AI": Z-Image Turbo bf16 + Qwen3-4B fp8 + ae; FLUX.2 Klein 4B fp8 + flux2-vae; LoRA Lenovo UltraReal, Z-Image-Turbo-Realism. Tất cả Apache 2.0. Workflow: workflows/api/zimagedailyt2i.json, workflows/api/flux2klein4bproduct.json.
Small English classifier behind the AI Frame browser extension, which marks paragraphs that read like AI while you browse. It runs entirely in the browser (transformers.js, ONNX int8); no text leaves the device. Labels: ai and human. "Reads like AI" is a signal about style, not a claim about who wrote a text. Trained with soft labels from desklib/ai-text-detector-v1.01 by Desklib (DeBERTa v3 large), released under the MIT License, alongside the hard labels (teacher weight 0.5). Thank you to Desklib for publishing it. Its licence notice: - RAID (Dugan et al., 2024), train split, domains news, reviews, reddit, wiki and books, no adversarial attacks, balanced human and AI, each document cut to…
These are the checkpoints the latents in were recorded from. E05-FT S01 is not the shipped atmosphere (S03). The kit/ folder holds what a 2015 atmosphere run needs besides the checkpoint: the initial condition (2015-01-03 12:00), two months of forcing (the inputs only), and configs for the control and each intervention. One run uses about 4 GB of GPU memory. Every config sets seed: 0. Keep it: the model is stochastic, and a shared seed is what makes two runs comparable node for node. Change the intervention by editing the overwrite block; make it longer with nforwardsteps (the forcing covers January and February 2015). To record the latents as well, and to steer a layer along a feature, use…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.