This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
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.
3,247Models
1,032Datasets
256Papers
2,088Publishers
5,461Sourced relationships
Updated 2026-09-20 · How the library is built
3,247 models, sorted by most downloaded.
Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
Anonymized checkpoint ladders for a study of safety behavior under RLVR self-play and SFT. Folders are codenames; the codename->setting mapping is withheld. (actor/modelworldsize.pt + config); use veRL modelmerger/convert2hf to load. step180 is HF safetensors.
Semantic-Lite-2 is a lightweight multilingual sentence embedding model that produces 256-dimensional semantic vectors. It is designed for semantic search, sentence similarity, clustering, retrieval, and retrieval-augmented generation (RAG) tasks. The model is built on top of the Spark-X2.5-1.7B backbone using a frozen-backbone plus trainable-projection-head approach. The 256-dimensional output keeps vector storage compact while preserving strong retrieval quality. Vectors are L2-normalized, so cosine similarity is computed as a simple dot product. Evaluated on 500 Indonesian NLI evaluation pairs (retrieval task, chance level 0.2%): Cross-lingual evaluation (10 languages, 20 pairs per…
Three chained PyTorch models (plus one exploratory variant) implementing the Each 200 ms / 400-sample sEMG window (2000 Hz, single channel, MyoWare-compatible) is routed through: intent detection → quality assessment → (optional) restoration. Operating thresholds (τintent = 0.40, τquality = 0.50) were selected via grid search on the validation split only, and evaluated once on a held-out test split. If you want to reproduce the pipeline exactly as described in Section 4.7 and Figure 1 of the paper, use only the first three files. The task-aware variant is a separate experiment reported transparently as a limitation, not a replacement for restorerbest.pt — see the "Downstream Motor-Intent…
Selected Group 1 epoch-200 model weights (manifest.json), plus historical flagship and scaling-law weights (manifestflagshipscaling.json). For the large-model batch, epoch = step / 1251. Historical 40x1024 is epoch440; 28x1152 is epoch80. L-wide/deep are epoch200; H no-dropout includes epoch200 and epoch280; H projdrop0.2 is epoch80. Latest is not a claim of best FID. The H drop0.1 attempt has no checkpoint and is not included. Public derivatives retain raw model, EMA(s), and allowlisted architecture configuration only. No optimizer, RNG, credentials, W&B identity or local paths are included. These are inference weights, not full training-resume backups. Original checkpoints remain…
Experimental PEFT adapter archive. The amber-cedar snapshot uses Qwen/Qwen3-30B-A3B-Instruct-2507, revision 0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe, with PEFT 0.20.0.
Public source distribution for SimCT / KDFlow experiments. This repository stores a Git bundle, not a direct Git branch mirror. The HF Git server rejects ordinary historical image blobs. The bundle retains the original code commit and complete Git history. - Runtime, checkpoints, datasets and untracked remoteartifacts are excluded. Download simct-b200-portable.bundle and source-manifest.json from the same pinned HF revision. Verify SHA-256 against the manifest before Git operations. First checkout in a new directory: For a subsequent complete bundle at the same absolute path, with simct-code checked out on the same branch and no conflicting changes: A Git bundle is a read-only transport…
Design × Engineering × AI. I build real, on-device systems from real data — space-tech tools, private AI, and honest engineering — and put the work where anyone can use it. - The Universe / Satellite Engine — a time-accurate, real-data solar system + live satellite orbits, built from NASA/JPL/ESA/CelesTrak data. Truth over spectacle. → sinhaankur.com/lab/celestial - Vera — a private, on-device AI twin of someone you love: local LLM, private memory, on-device voice. Nothing leaves your machine. Part of the Unhosted family. - Unhosted — pooling the hardware you own into a private inference cluster. AI that lives where you do. - rag-engine — a small, fully on-device Retrieval-Augmented…
This model was converted to GGUF format using Unsloth. - gemma-4-E2B-it.BF16-mmproj.gguf - gemma-4-E2B-it.Q4KM.gguf
Trained on HAM10000. Educational project — not diagnostic-grade, not a medical device.
Trained on ISIC 2017 Task 1 lesion-boundary masks. Educational project — not diagnostic-grade, not a medical device.
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…
This repository contains a fine-tuned XLM-RoBERTa Base model for 3-class sentiment classification in Sindhi. The model predicts one of the following labels: - positive - neutral - negative This model is intended for research, experimentation, and lightweight production prototyping on Sindhi-language sentiment analysis tasks. It may be useful for short-text classification such as reviews, feedback, and social-media snippets. It should not be used as the only basis for high-stakes decisions. Performance can vary by domain, dialect, and writing style, and human review is recommended when errors could affect people or organizations. The saved model artifacts in this repository report the…
An ACT (Action Chunking Transformer) imitation-learning policy trained on an SO-ARM101 follower arm equipped with an AmazingHand dexterous hand, performing a cube pick-up task. - 20 teleoperated demonstrations, 18,538 frames, 30 fps - The cube was recorded at 4 different table positions, 5 episodes each, to cover positional variation shoulderpan.pos, shoulderlift.pos, elbowflex.pos, wristflex.pos, wristroll.pos, gripper.pos Requires an environment matching this project (a customized lerobot that includes the soamazinghand robot definition): 1. --task must match the training singletask string exactly 2. Camera names and index order must match training 3. The robot must already be calibrated…
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.