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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
Research artifacts. Details will be provided after publication.
Private research checkpoints — Cosmos-Predict2.5-2B robot/multiview-agibot (frameinit) post-trained on RoboCasa CoffeePressButton 3-view renders (55 human demos, 704² → trained at 480², 81 frames/view @20 fps). 2026-10. iterXXXXXX/modelemabf16.pt — EMA weights (bf16) for inference with experiment 2brobocasacoffeeagibotframeinit (code/ holds the dataset/experiment files and mvinfer.py needed to reproduce; see code/HANDOFF.md). Research only (world-model video generation for IDM labelling / policy training). Round 1 (iter004000/, identity extrinsics) showed view collapse at t≥20 frames (3 views converging to one), seed-dependent. Round 2 restarts from the agibot base with real MuJoCo camera…
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…
A small Albef implementation for Generation, packaged with an explicit configuration and an initialization checkpoint. The large variant is a reproducible starting point, not a trained model release. - 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 adafactor with a linear warmup schedule. These are starting values in the script, not…
This model is a fine-tuned version of Agtian/cpt8. It has been trained using TRL. This model was trained with SFT.
Single-frame crack segmentation for laboratory fracture images: concrete and cement-mortar cubes under uniaxial compression, coal / sandstone Brazilian discs under SHPB impact. Trained on the CrackEvo benchmark (China University of Mining and Technology-Beijing laboratory experiments) plus public crack datasets (CFD, CrackTree, DeepCrack, 40k) used for training only. Large images are processed with 512-px sliding windows (stride 384, logits averaged). About 45 ms per tile on a desktop CPU with ONNX Runtime. Exact-pixel IoU is limited by centre-line ground truth on two of the sources; tolerant F1 reflects localisation quality. Cross-material generalisation (unseen sandstone) drops to IoU…
A 32M-parameter classifier that reads one line of code, config, log or prose and scores whether the line holds a literal credential: a password, API key, access token, private-key header, connection string with a password, or an Authorization header value. It runs on a laptop CPU in about 4 ms per line, so you can score every line of a trace, log or diff before you share it. Use it next to a rule-based scanner, not in place of one. On 1,264 human-written lines it finds about 4 in 5 credentials and is right about 9 times in 10 when it flags a line. It also misses about 1 credential in 5, so it must never be the only check before you publish a file. predictonnx.py applies the temperature…
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.