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.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
A 70.8M-parameter text-to-image model for pixel art, flat icons and stickers. Small enough for laptops and, next, the browser. Part of Darkroom, a project to build tiny image generators that run where you are. The same held-out prompts, never seen in training, from the 0.6B teacher and from each Pinhole size: ONNX Runtime Web (WebAssembly or WebGPU), so prompts and images never leave the browser. Parity: the ONNX-only pipeline matches PyTorch to within a mean of 0.00/255 per pixel (same noise, 20 steps). The browser's tokenizer is checked against Python by the demo's Self-test button. Start with one of the three styles, then describe a simple subject: of a. - Adjust guidance with cfg: 3–6…
A 34.4M-parameter text-to-image model for pixel art, flat icons and stickers. Small enough for laptops and, next, the browser. Part of Darkroom, a project to build tiny image generators that run where you are. The same held-out prompts, never seen in training, from the 0.6B teacher and from each Pinhole size: ONNX Runtime Web (WebAssembly or WebGPU), so prompts and images never leave the browser. Parity: the ONNX-only pipeline matches PyTorch to within a mean of 0.00/255 per pixel (same noise, 20 steps). The browser's tokenizer is checked against Python by the demo's Self-test button. Start with one of the three styles, then describe a simple subject: of a. - Adjust guidance with cfg: 3–6…
A 13.1M-parameter text-to-image model for pixel art, flat icons and stickers. Small enough for laptops and, next, the browser. Part of Darkroom, a project to build tiny image generators that run where you are. The same held-out prompts, never seen in training, from the 0.6B teacher and from each Pinhole size: ONNX Runtime Web (WebAssembly or WebGPU), so prompts and images never leave the browser. Parity: the ONNX-only pipeline matches PyTorch to within a mean of 0.00/255 per pixel (same noise, 20 steps). The browser's tokenizer is checked against Python by the demo's Self-test button. Start with one of the three styles, then describe a simple subject: of a. - Adjust guidance with cfg: 3–6…
Bienvenido al repositorio de checkpoints de entrenamiento (.ckpt) del modelo de texto a voz Capybara. Este modelo ha sido entrenado utilizando Piper. Capybara es un modelo de texto a voz para español de México. Este repositorio contiene sus archivos de checkpoint (.ckpt) generados durante el entrenamiento con Piper. - Continuación del entrenamiento: Los checkpoints permiten reanudar el proceso de afinación con Piper-Neo. Esta licencia establece las condiciones bajo las cuales se permite el uso de este modelo de voz. Al utilizar este modelo de voz, aceptas cumplir con estas condiciones. - Crédito: Dar crédito al creador del modelo es opcional, pero se agradece. - Prohibido el uso en…
[Español abajo / Spanish below] This model has been automatically converted from Piper Train checkpoints to ONNX format using Piper ONNX Converter. This model is optimized for use with Piper TTS. For Windows users, we recommend using ONNX TTS by HirCoir - a user-friendly GUI application: - Easy-to-use interface for text-to-speech conversion - Optimized for ONNX models like this one - esMX-capybara.onnx: Main ONNX model file - esMX-capybara.onnx.json: Model configuration file Este modelo ha sido convertido automáticamente de checkpoints de Piper Train a formato ONNX usando Piper ONNX Converter. Este modelo está optimizado para usar con Piper TTS. Para usuarios de Windows, recomendamos usar…
FP16 Quantized Voices for Piper text to speech system, original voices. For checkpoints that you can use to train your own voices, see piper-checkpoints
Unofficial ONNX export of pixai-labs/pixai-tagger-v1.0 in FP32, FP16 and INT8. Runs with ONNX Runtime only. - Input input: float32 [batch, 3, 1008, 1008], RGB, NCHW. - Output prediction: float32 [batch, 30877], probabilities (sigmoid is part of the graph). - Opset 17. training cutoff, and are also scored against their Danbooru tags. - FP16 is meant for GPUs. On CPU it is slower than FP32, because the CPU has no native FP16 math. - FP32 fits on a 4 GB GPU only barely (3.2 GB VRAM), and its speed varies between runs. - INT8 speed depends on CPU support for VNNI instructions. On older CPUs it can be slower than FP32. - Peak RAM on CPU includes session initialization; FP32 briefly reaches 9.4…
Test-time-training models with a transactional safety harness, and a study of what they can learn lastingly from experience. plastic studies models that learn while they read: their memory is a set of fast weights that take gradient steps on a self-supervised objective as tokens arrive, so context is compressed into weights instead of a growing cache. Three substrates share - PlasticCore, a from-scratch model of about 6M parameters: a selective state-space recurrence plus a delta-rule fast-weight memory, meta-trained end to end through its inner loop. The same block stack serves next-token prediction on text and a hidden-friction control task where the only way to predict well is to infer…
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.