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 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Training campaign launched 2026-10-10. Training is ongoing; no completed RT results are claimed. - All seven runs are independent, with no dependencies or evaluation waits, per the user's later instruction. - These two phase-2 launches also override their evaluation wait at the user's request; no Gate 1 result is claimed. - Remaining 13 executable recipes retain their gates. R-FM remains undefined and deferred. - All seven runs now use standalone batch jobs. Interactive allocation 4857 was canceled after saving RT-A2 at update 58. There is no additional backup job. Every run uses global batch 128, all 277,713 windows from 1,712 available LIBERO demonstrations, and ten epochs (21,700…
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).
İpek Öztaş, Duygu Ceylan, Aybars Buğra Aksoy, Ayşegül Dündar This repository contains the RoPEMover LoRA (rank 16) for RoPEMover moves an object in a single image by manipulating a depth-aware 3D extension of the diffusion transformer's rotary positional embeddings. This LoRA requires the modified pipeline from the RoPEMover code repository. It does not work with the stock Qwen-Image-Edit pipeline. The weights are downloaded from this repository automatically. Apache 2.0. Use is also subject to the license of
Checkpoints of Roboactualizer, a joint image/action flow-matching policy on a frozen V-JEPA 2.1 ViT-L/16 encoder. Each folder holds the checkpoint, the datasetstats.json used for action/state normalization (keep it next to the checkpoint; evaluation looks for it there) and the training config.yaml. SHA256SUMS lists file hashes. All checkpoints use the 300M V-JEPA 2.1 encoder: https://dl.fbaipublicfiles.com/vjepa2/vjepa21vitldistvitG384.pt, saved as checkpoints/vjepa2/vjepa21vitlarge384.pt in the code repository.
Submission checkpoints for the RoboSynChallenge. The runnable code and evaluation commands are in the GitHub repository. The checkpoint path in the second column is the weight used for the corresponding row. Success rates: official random configuration, official scripts/evalpolicy.py (expert pre-check on), RTX PRO 6000, five seed streams x 100 episodes. sampleloading has two policies: π0.5 (policy/fmc3no1) and policy/perceptionclosedloop (image estimators plus a closed-loop scripted expert; its two networks are the files under sampleloading/perceptionclosedloop/v3, it reads no object state from the simulator). The manipulatepipette entry is a 10-stream validation (1000 episodes) of the same…
DIF end-effector models trained on RoboTwin with batch size 64, validation ratio 0.1, and point-cloud policy dimensions [256, 512, 1024]. Each task checkpoint is stored as checkpoints/ ee5000bs64robotwin/5000.ckpt.
面向 ALFWorld 与 WebShop 的 Qwen2.5 强化学习模型。所有模型与算法统一以 step200 为比较基准，training seed=0，thinking off。训练采用 GiGPO。 表中链接标明可下载的 checkpoint；WebShop 1.5B Stable Gaussian 主结果采用 step200。 下载内容为 LoRA adapter，需搭配对应的 Qwen2.5-Instruct 基座使用。训练与评测入口见 GitHub 项目。
static quants of https://huggingface.co/haohuiliang/RobustOps-SFT-4B
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.