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 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
Autoregressive video world model for Minecraft. A frozen Stable Diffusion VAE compresses 256×256 frames to 4×32×32 latents; a decoder-only transformer (338M params, RoPE, DWS-style per-block action adapters, SDPA flash attention) predicts the next latent conditioned on discrete action tokens (29 ids: 22 keyboard + 6 camera bins + 1 no-op). Each run directory holds a full resumable state (model + optimizer + scheduler + step/epoch counters), so any file below restarts training bit-identically with the matching config and --resume: Weights derive from public TESS-VLA gameplay data and an sd-vae-ft-mse base. No license is asserted here — check the dataset and base-model terms before commercial…
This is an unfinished training run, published as it goes. Checkpoints appear here every half epoch of a planned six. Nothing here is a final result, and the numbers below will move. A DFlash draft head for Qwen/Qwen3.6-27B, trained as a reproduction of DFlash (arXiv:2602.06036) on a model the paper does not cover. Full method, scripts and measurement records: It is not a standalone model. It cannot generate text by itself. It writes a 16-token block in one forward pass, conditioned on the target's hidden states at layers 1/16/31/46/61, and the target then verifies that block in a single pass and commits the leading run that matches. Greedy verification means the committed tokens are exactly…
Sam Goldman. MIST checkpoints from Zenodo record 8316682. The deposit identifies the model/data licence only as other-open; no exact licence terms have been identified. This reports the upstream category, not a new licence grant or an application of the software's MIT licence to the checkpoints. The upstream designation does not identify specific licence terms; the Hub category is unknown. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.
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).
ResNet-18 binary (benign vs. malignant) classification checkpoints, trained per-dataset on eight public breast-imaging sources spanning ultrasound, Full write-up, methodology, and comparison to each source paper's own These are single-modality baselines, not the 3-branch fusion model. Each checkpoint is models.SingleBackboneClassifier (one ResNet-18 backbone, ImageNet-pretrained, first conv adapted for non-RGB inputs where applicable) — see models/backbone.py / training/train.py in the repo for the loading code. For the 5-fold datasets, this is one fold's checkpoint, not an ensemble or the averaged model — reported accuracy is the 5-fold mean from the full report for context, not this…
A research-oriented Mocov3 prototype targeting Generation. The included small setup documents defaults and file formats without presenting unverified performance numbers. - 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 lamb with a step schedule. These are starting values in the script, not evidence of a completed run. For a…
MSBuddy project, maintained by Shipei Xing. Original database files from msbuddydatav0.2.4 and LightGBM model from msbuddydatav0.3.0. Apache-2.0 is recorded on the basis of the upstream project licence; the release pages do not state separate asset-specific terms. Its application to these files is an inference, not an independently confirmed per-file licence grant. The original licence is retained as db0.2.4-ml0.3.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.
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).
A structured set of research notes on Multimodal Reasoning, 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 VQAv2, GQA, and NLVR2 - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.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 raw logs. The note is intentionally…
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.