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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
HFD研究持续交互中的人形机器人全双工行为：接收语音与可选的图像/视频，连续生成动作，并在视觉问答时生成语音回答。 任务包括 turn-taking、interruption（含stop）、backchannel、hesitation、pause。视觉问答叠加到这些交互任务中，不另设独立的SWA任务。代码中保留的swa名称用于模块与历史配置兼容。 直接执行bash inferrenderauto.sh会选择兼容的最新run并持续监听。 该入口接替原inferrenderautomultiturn.sh；任务集合、train/val样例、多卡队列、 checkpoint间隔和异步渲染逻辑不变。无需另起内部worker，也不要同时启动两份自动推理。 训练环境复用.venv-psi。依赖、模型和数据路径必须可用，启动器会检查；不要把配置打印成功当成完整训练验证。关键参数在训练脚本顶部，固定配置与环境变量映射在当前配方。完整续训与WandB续接参数见训练脚本末尾说明。 backup/preparefullduplexcontinuousaudio.sh用于连续背景音数据重建（旧流程还会导出hidden）， backup/prepareomnitrainingdata.sh及其调用的backup/cachevideoomniembeds.py 不要为清理目录而重新执行造数据。旧canonical-motion消融、旧OSS索引预构建、 早期hidden包装入口已退役，恢复方式见清理记录。…
Contributors: Songlin Wei, Hongyi Jing, Boqian Li, Zhenyu Zhao, Jiageng Mao, Zhenhao Ni, Sicheng He, Jie Liu, Xiawei Liu, Kaidi Kang, Sheng Zang,Weiduo Yuan, Marco Pavone, Di Huang, Yue Wang $\Psi0$ is an open vision-language-action (VLA) model for dexterous humanoid loco-manipulation. Our model first learns task semantics and visual representation from large-scale human egocentic videos, and then is post-trained on a smaller amount of real-world teleoperated robot data, to learn general dynamics of the embodiment. Our foundation model is capable of acquiring new long-horizontal dexterous loco-manipulation skill by fine-tuning using as few as 80 trajectories. Our key finding is that scaling…
A LoRA adapter fine-tuned with QLoRA on Llama 3.2 3B Instruct to review Python code with severity levels (Critical, Warning, Info). Trained using Unsloth on Google Colab.
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).
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.