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.
Sori-1B-MCQ — sori (소리) is Korean for sound — listens to a clip and answers typed questions about it: which of these options, how far along this scale, is this true. It returns the answer, a probability for every option you defined, and a confidence — never prose, never a token stream to parse. Code stays in charge: your program defines the options, reads the numbers and decides what to do. It is the decision model of the Sori Agent project at the Human Interface Lab, Seoul National University. Inspired by TypeSafe's System One model, Jev. Jev showed what a model built for software rather than for conversation looks like: a state, typed questions, calibrated probabilities, one call for many…
A ~10 million parameter Small Language Model (SLM) Transformer designed for binary classification to detect whether Python source code was written by a human or generated by AI. The model was trained on a dataset of human-written code extracted from GitHub (Q3 2017 and prior) and synthetic AI-generated code mimicking Q3 2026 generative AI paradigms. You can test the model interactively without writing any code by visiting our Gradio Web UI Space. To use this model locally, you must include the model architecture class in your script before loading the weights. Once the class is defined, you can automatically pull the weights from Hugging Face and run inference cleanly using this helper…
The Sovereign-Swarm-Coherence-v1-alpha repository contains the lightweight ONNX & WebAssembly (WASM) client-side package for running inference directly inside web browsers and mobile apps with 0ms server latency and zero cloud API infrastructure costs. - 100% Offline Capable: Zero network dependencies after initial WASM module download. Apache 2.0 License © ItsNotAI LABS
Private backup of trained policy weights (.pt only, no optimizer states).
A lossless repack of stepfun-ai/Step-5-Preview-NVFP4 (revision bc246cc2848e17b37f377eda8fd825c5b3018d1f), 17.1 GB smaller. The NVFP4 block scales of the routed experts are stored in b12x's compressed scale format (CSF), the lossless format introduced with nvidia/Qwen3.8-Flash-Next-FP4-Local. Every FP4 weight byte and every other tensor is identical to StepFun's checkpoint, and every scale decodes to StepFun's exact bytes, so this checkpoint computes exactly what Step-5-Preview-NVFP4 computes. Step 5 Preview is StepFun AI's model: a sparse mixture of experts with 600B total and 27B active parameters, text and image input, up to 1M tokens of context. Model card: stepfun-ai/Step-5-Preview.…
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.