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 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
LoRA fine-tune của π0.5 (pi05base) cho tay máy Yahboom X3Plus (5 khớp + gripper, 20 Hz, 2 camera), nhiệm vụ "pick up the red cube and put it in the bowl". - Chưa kiểm chứng trên robot thật. Mọi số liệu ở trên là loss huấn luyện. - Loss đi ngang từ bước ~4.000. 6.000 bước sau chỉ giảm thêm 36%, trong biên độ nhiễu. - Một nhiệm vụ, một bối cảnh, một bộ camera. Nhiều khả năng hỏng khi đổi vị trí - Camera cổ tay (USB webcam) cho ảnh mờ, nhiều frame gần như trắng khi áp sát mặt bàn. Cần config pi05x3pluslora và lớp LeRobotX3PlusDataConfig tương ứng trong openpi (ánh xạ astrargb → base0rgb, usbcam → leftwrist0rgb, delta mask (5, -1)). yahboomx3plus · joint1–5 tính bằng radian (URDF x3plusarm)…
LeRobot policy checkpoints uploaded by goalgen/uploadhfcheckpoints.sh. Each subfolder contains the deployment-ready pretrainedmodel/ payload (model.safetensors + config.json + pre/postprocessor + trainconfig.json).
π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 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…
A π0.5 (openpi) policy fine-tuned for Unitree G123 + Dex1-1. The task is to pick up the leftmost object of the requested type in the target area. Only the left arm moves; the right arm stays at its initial pose. This is the openpi checkpoint from step 19999, the final step. It contains params/ (the weights) and assets/ (norm stats). The optimizer state (trainstate/) is not included. The model was trained on exactly two prompts. Pass them verbatim at inference time: The policy also accepts JPEG bytes for the images; they are decoded as BGR and converted to RGB. Images are resized to 224×224 inside the model. In the raw data, this is the concatenation of states/actions → leftarm.qpos(5) +…
pi0.5 trained on with LeRobot 0.6.1 to pick up, among seven tiles laid anywhere on a foam pad, the mahjong tile an instruction names, and place it in a tray, with an SO-101 arm in the Isaac Lab task Mahjong-Tray7TwosFaces-SO101-v1. Instructions read Pick up the {name} tile and place it in the tray. Trained in simulation only; it has not been run on a real arm. It goes on from the step-002500 checkpoint of which held over the named tile in closed loop. A trace of it found it deciding its descent by its joint state alone: held a few degrees off the hover pose its pushed demonstrations went down from, it went down once given that pose's state, and changed nothing with either camera blacked…
Fine-tuned from tc-huang/pi05mahjongv1tray07twosdetour (revision step-015000) with LeRobot 0.6.1 on the LeRobot dataset mahjongv1tray07twosdetour (run pi0520261014031144), with the pi05 preset of scripts/trainlerobot.sh and --steps=15000 --policy.optimizerlr=1e-5 --policy.schedulerwarmupsteps=250 --policy.schedulerdecaysteps=15000 --policy.schedulerdecaylr=2.5e-6 --savefreq=5000 --logfreq=200 --policy.freezevisionencoder=false --policy.gradientcheckpointing=false --wandb.enable=false: in float32, the vision encoder frozen unless those arguments unfreeze it. Its preprocessor does not crop the cameras. State and actions are normalized by the quantiles of all the dataset's frames, not by the…
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.