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 2026-10-21 · How the library is built
12,010 models, sorted by most downloaded.
Pretrained checkpoints for the interactive world simulator (umi-wm / iws-model): an action-conditioned latent world model with a joint vision + tactile branch. The matching data lives in the dataset repo of the same name, Superseded releases are kept under legacy/ for reproducibility. Do not use them as defaults. Checkpoints for other world models, trained on the same data for comparison. They are not IWS models and do not follow the /checkpoints/ layout above. Most tasks have all four files. rubik and rubikeasy2cam currently ship stage 1 only — their wm.ckpt does not exist yet, because stage 2 has not been run for them. Every.yaml was verified to rebuild the model and load its checkpoint…
A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token cdn3 woman to invoke the concept.
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).
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).
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).
SmolLM2 (Llama) mimarisiyle sıfırdan eğitilmiş modelin GGUF sürümleri. Eğitim token sayısı: ~/.cache/lm-studio/models/bcckfdn/cevher-test-10-lc-it-4-GGUF/ altına koyun ya da uygulama içinden arayın.
SmolLM2 (Llama) mimarisiyle sıfırdan eğitilmiş modelin GGUF sürümleri. Eğitim token sayısı: ~/.cache/lm-studio/models/bcckfdn/cevher-test-5-GGUF/ altına koyun ya da uygulama içinden arayın.
A QLoRA fine-tune of Ministral 3 14B Instruct for cfoperator, an autonomous monitoring agent for a Kubernetes homelab. It replaced a 26B general model on that path at roughly 5x lower latency with the same accuracy on the project's triage suite. This is not a general assistant. It was trained on exactly one system prompt and answers with exactly one JSON shape. Outside that prompt it is just a slightly bent Ministral. Read What it is not before downloading 8 GB. The vision projector (mmproj) is not included. Triage is text-only and the training run never exercised the vision layers. With ollama, either pull straight from the Hub or build from the Modelfile. The Modelfile is the…
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). - PEFT 0.21.1
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.