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.
12,010Models
3,413Datasets
288Papers
7,298Publishers
12,421Sourced relationships
Updated 2026-10-21 · How the library is built
12,010 models, sorted by most downloaded.
Torque-safe locomotion policies for the BorderCollieRobot quadruped (0.5x scale, 8x RS01 + 6x RS00). Gate: Hard gate checks delivered torque vs RS01 17 N.m / RS00 14 N.m. MARGINAL = hard limits not violated, but P99 > 70% soft threshold. All three cleared the hard limits; zero FAILs under the new delivered gate. - policy.onnx - Deployed policy ([1,61] -> [1,14], 50Hz) - model199.pt - PPO checkpoint (200 iters) - evalmeta.json - Evaluation metadata - effortlimit = 17/14 N.m (peak) - Other weights frozen at Phase 3 trial #7 Phase 4 (after P0 motor identification): full fine-tuning to push MARGINAL -> PASS. See collierlphases.pdf for the complete story.
static quants of https://huggingface.co/NbAiLab/borealis-1b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/borealis-1b-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
weighted/imatrix quants of https://huggingface.co/NbAiLab/borealis-1b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/borealis-1b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
Botfilter is a browser extension that scores English text for “likely AI-written” as a calibrated probability, on the device. A score can be wrong and should not be used as proof of authorship. Each releases/ / directory contains the ONNX model, the WordPiece vocabulary, the calibration settings, and a lockfile with SHA-256 checksums. Use all files from the same version. The extension’s inference implementation defines preprocessing, window aggregation, and calibration; these files do not provide a Transformers pipeline. Human text: openly licensed news articles from Common Pile (only documents whose metadata records CC BY 4.0), Foodista food blog posts (CC BY 3.0), UK Parliament Hansard…
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.
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). - PEFT 0.13.2
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). - PEFT 0.13.2
Quantized weights of Qwen3-4B in the native.brasa format of Brasa, a local inference engine for Apple Silicon written in Rust on Metal. Brasa serves an OpenAI- and Anthropic-compatible API on your Mac and is built mainly to run coding agents (Claude Code, Codex, Cline, OpenCode) against a local model. These files only work with Brasa. They are not GGUF, MLX or safetensors. Each variant is a folder that Brasa loads as one model (model.brasa + the Qwen3 tokenizer): - q40: blocks of 32 weights along the input dimension, an f16 scale and 16 bytes of nibbles (4.5 bits per weight), w = d · (q − 8). - q60: blocks of 32, an f16 scale and 6-bit values (6.5 bits per weight), w = d · (q − 32). - q80…
This repository contains the weights for a 3D U-Net model trained from scratch on the BraTS 2020 dataset for volumetric medical image segmentation. The model identifies and segments complex brain tumor sub-regions using multi-modal MRI scans (T1, T1ce, T2, FLAIR). The training pipeline was engineered using PyTorch and MONAI, featuring Automatic Mixed Precision (AMP) and overlapping sliding-window inference optimized for 16GB GPU constraints. (Note: Upload the Multi-Planar 3D reconstruction and Class-Wise Error Map images you generated in Kaggle directly here using the image upload button in the Hugging Face editor.)
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.