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SAVRN Model Hub
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.
Latent-belief RL on the passive BayesClue detective game. Base: Qwen3.5-4B. Belief is read from forced-choice probe logits (never verbalized). - Merge the LoRA with credalverl08/remergesftqwen35.py (the model.layers.→model.languagemodel.layers. remap); the stock verl.modelmerger writes a base copy. - Reward R = α·(−CE(pH,qH)) + (1−α)·means(−CE(pR,qR)), α=0.6. The −1.225 reward plateau IS the optimum −H(p), not a truncation artifact.
Muhammad Faizan Khan. ChemEmbed positive-mode model and paired reference database from release v1.0.1. The computational-metabolomics maintainers record MIT for the model based on the upstream project licence, not a confirmed checkpoint-specific grant. Upstream CITATION.cff also describes a CC-BY-4.0 models/data deposit, but the exact released model's relationship to that deposit has not been established. The reference database is separately CC-BY-4.0; upstream documents its Parquet conversion from the cited Zenodo data. No conversion is performed by this mirror. Per-file licences below override this default; the model-card licence field does not relicense accompanying data. The software…
Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…
This repository contains the CI-Net processing, training, inference, and validation code. The directories under code follow the processing order: 1. datapreparing: read and align satellite and radar inputs. 2. labeling: create cloud labels. 3. finalpreprocess: convert the prepared fields into model input files. 4. training: train the model and run inference. 5. validation: create validation targets and calculate validation metrics. Each code directory contains Python files in src and its configuration and shell entry points in run. rawdata contains the input example included with this repository. result contains prepared data, model files, inference outputs, and validation outputs. See…
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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 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
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
Recognition and synthesis models, grouped so the two directions are easy to compare.
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.
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.
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.
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.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.
