ALMA: Airborne LiDAR Masked Autoencoding Framework for Self Supervised Representation Learning on 3DEP Point Cloud Arnav Goel and Jinha Jung reconstruction models, and complete final segmentation models. Download and verify using the code repository's manifest/downloader. SHA256SUMS lists the exact exported weight hashes. Optimizer states are omitted. Inputs are 20,480 XYZ points. Coordinates must be in feet. Pretraining uses bounding-box-centered XYZ divided by [50,50,25] (QL1) / [100,100,75] (QL2). Downstream uses mean-centered XY divided by half crop width and minimum-shifted Z in feet. Configurations are not interchangeable. The segmentation decoder predicts LAS 2–6; other training…
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.
12,283Models
3,510Datasets
290Papers
7,436Publishers
12,632Sourced relationships
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.21.2
Self-contained x86-64 ELFs compiled by the Alpha compiler from one.alpha closure per system/target pairing. Each drives an NVIDIA GPU directly through the RM/UVM interface; there is no CUDA, Python or framework in the executable. They train, resume from a checkpoint and predict through the versioned text-artifact request protocol. manifest.json is the authority for the current sha256 of every file. Only the RTX 3090 pairings have been run on physical hardware.
LoRA checkpoints and training metrics from an internal reinforcement-learning trial. Research artefacts only; not intended for use. Contents are documented in MANIFEST.md.
A general-purpose chat model on Qwen3-4B that knows when to call a web-search tool, and when not to. Part of the Amethyst family. Amethyst 2 Mini (Failed) keeps a direct, conversational voice and adds reliable web-search tool calling: search when the answer depends on current information, answer from knowledge when it does not, decompose multi-part questions into several queries, and summarize retrieved snippets into a cited answer. Do not disable thinking (enablethinking=False, --reasoning off, or a hand-built prompt that ends in an empty block). The training data taught tool calls in turns that have no empty think block, so with one present the model tends to answer in prose instead of…
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 3 - Transformers 4.57.6 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.22.2
Public project checkpoint repository. Open a method to see its seeds directly: checkpoints/ / / /seed- /best.zip When the same method/seed has multiple distinct configurations, each archive is stored inside a configuration folder below that seed. Configuration names describe the actual method or protocol; dates belong in metadata. Never overwrite different weights. Strong LoRA A/B and LSR lambda comparators have explicit method labels in the index. The verified reorganization preserved all 90 final archives and all existing score-bearing file contents. All 16 previously unknown archive seeds were verified as 42 from archived training arguments. Actual ES4/ES10 settings remain unchanged.…
Model assets for AMTFlow, a modular instrument-conditioned automatic music transcription pipeline (instrument tagging → instrument-family routing → MuScriptor). AMTFlow downloads these files on first use; you do not need to fetch them by hand. One classification head per frozen audio encoder, trained on OpenMIC-2018 (20 instrument classes). AMTFlow's probe tagger runs the encoder, applies the head to one layer's time-averaged features, and marks an instrument family present when any of its classes reaches that class's threshold. Several taggers, including audio-language models, can be combined by union or majority vote. Test mAP is the mean over five probe seeds with a 95% t-interval over…
This repository contains the fixed model files used by the AmySeg inference workflow. It is a model bundle, not a standalone executable. Runtime code and algorithm configuration are provided by the AmySeg Python package. The nnUNet dataset and plans JSON files are bundled with the Python package. The model weights in isonet2/model.pt and nnunet/checkpoint.pth are licensed under CC BY-NC 4.0. The full license text is included in LICENSE. Non-commercial research use is permitted under the license terms. Non-commercial sharing and adaptation are also permitted, subject to attribution and the other license conditions. Commercial use is not authorized by this license and requires separate…
Qwen3-0.6B fine-tuned to give a scanned document a short, useful name from the text the phone's OCR reads. It is the naming model of Anascan, an on-device document scanner: the app downloads this file once, checks its size and SHA-256, and runs it with llama.cpp on the phone. No document data leaves the phone. Input: the app's chat prompt (below) with the naming evidence it builds from OCR (headings, issuer, dates and distinct lines, at most 600 characters), thinking disabled. Output: one name in the document's language, 3–8 words, saying what the document is and who issued it or what it is about. The app checks every name on the phone (grounded in the document's words, no numbers the…
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.