A(n) APPO model trained on the doomhealthgatheringsupreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ After installing Sample-Factory, download the model with: To run the model after download, use the enjoy script corresponding to this environment: You can also upload models to the Hugging Face Hub using the same script with the --pushtohub flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details To continue training with this model, use the train script corresponding to this environment: Note, you may have…
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.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
This archive stores reproducible RLinf/OpenVLA-OFT LIBERO training recipes, model artifacts, checkpoints, logs, and evaluation summaries. This model archive is intentionally separate from the independent /media/david/HDD/trainingrecipe/ repository: - models/: base VLA model artifacts. - checkpoints/: distributed PPO checkpoints by training run and global step. - results/: metrics, logs, and TensorBoard outputs by training run. - runs/: raw logs and TensorBoard snapshots. - /media/david/HDD/trainingrecipe/: one self-contained recipe directory per training run, containing only YAML, source revision, hyperparameters, and README. The first archived run is the 4-GPU H20 task-3 PPO experiment…
Meta SAM 3 (Segment Anything Model 3), exported to ONNX (FP16) for a C++ ONNX Runtime pipeline: a vision encoder, a text encoder and a decoder with a dynamic prompt-count batch (a single file accepts any number of text prompts at runtime, no re-export needed). The model is derived from Meta's SAM 3 and is provided under the SAM License (see LICENSE in this repository). Use, reproduction and redistribution are subject to that agreement; the license text must be kept with any redistribution.
samai-27b（Qwen/Qwen3.8-27B 手术版：PonderNet 停机位点 L35/39/43/47/51/55/59/63 + dMoE 8 专家 + MTP）经 QLoRA SFT 后，按 mixbit 静态化管线产出的 llama.cpp 混合精度权重。 等价稠密模型 ≙ Qwen35ForCausalLM（48 linearattention + 16 fullattention，hidden 5120），可直接用上游 llama.cpp 载入。 - Token embedding / output：Q6K（--token-embedding-type Q6K） OpenAI 兼容端点 /v1/chat/completions。 - 基座：tchbcb/samai-27b（含 SFT adapter / 训练日志 / 数据） - 配方参考：tchbcb/samai-4b-mixbit
Deep-learning-based missing-trace reconstruction on pre-stack seismic shot gathers. Given a shot gather with masked (missing) traces, the model reconstructs the full gather. Each experiment is one architecture trained on one missing-trace scenario with one random seed. Model directories are suffixed with the dataset the model was trained on: - mobil — Mobil field dataset (pre-stack seismic shot gathers) - segc3 — SEG C3 synthetic dataset (wiki.seg.org/wiki/C3): 9 regular shots, 201 traces x 625 time samples, dt = 2 ms - Chai2020 UNet (chai2020unet) — 2D U-Net (Chai et al., 2020, IEEE TGRS, DOI 10.1109/TGRS.2019.2961015): 50 layers = 19 convolutions (5x5, same padding) + 18 ReLU + 4 max-pool…
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. 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.
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. 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.
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. 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.
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.
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.
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.
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.
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…
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…
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…
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.