pipelinetag: text-classification - mlnet - text-classification - moderation - toxicity-detection An ML.NET text classification model trained to detect toxic, abusive, and inappropriate language in chat messages for the ConCord chat application.
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.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
A structured set of research notes on Cross Modal Fusion, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with reading.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and…
Working implementation of Perceiver for Generation using a small configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a polynomial schedule. These are starting values in the script, not evidence of a…
Segments nuclei from the host cell mask channel alone — no nuclear stain required. A cross-channel model: it is given the cell image and predicts where the nuclei are, freeing the DAPI/Hoechst channel for another marker. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Cross-channel nuclei-from-cellmask (Cellpose-SAM) in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. Point spaCR's mask generation at the downloaded…
A convolutional autoencoder trained to remove Gaussian noise from 256×256 RGB images. The model learns the manifold of clean images and projects noisy inputs back onto this learned space. This model removes Gaussian noise from corrupted images by learning to map noisy images back to their clean versions. During training, the autoencoder learns the distribution of clean images, enabling it to filter out noise while preserving image content. - σ=75 (on 0-255 scale) Gaussian noise - Approximately 0.294 on [0,1] normalized scale This model is designed for educational purposes to demonstrate how autoencoders can learn to denoise images by understanding the structure of clean data. - Exploring…
CPU-only page classifier for text extracted from PDFs (no OCR), designed for, "label": one of invoice|lab|radiology|dischargesummary}, split train/val/test. Point steps 2-4 at it with --data-dir; re-calibrate thresholds on your val split before deploying. Note traintransformer.py targets transformers==4.57. — 5.x removed several TrainingArguments kwargs this script uses. - Live-metrics trackio dashboard could not be hosted on this account (Gradio Spaces require PRO), so run metrics live in the job logs.
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.