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.
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 0.21.0
Public, reproducible inference for the Tartan IMU Challenge (IROS 2026). Task. Given 6-axis IMU windows (acc 3 + gyro 3, length 200) covering a motion window plus its temporal context, predict body-frame velocity (vx, vy, vz) for all four platforms (car / dog / drone / human) with a single unified model. Model. lihuaimuiros2026v1: a shared ResNet1D encoder with an FFT branch, followed by a Transformer over a triplet of consecutive windows (prev / curr / next), and a 4-expert Mixture-of-Experts head. The expert gate is a soft router driven purely by IMU-derived features — experts are learned motion regimes (general / slow / fast / agile), not platforms. A single frozen checkpoint (seed 0) is…
Launch manifests for the lil launcher. Each entry is a directory holding one lil.yaml that names the repository holding the weights, optionally pins the commit the local inference lab qualified, and states the serving policy the launcher cannot read from the checkpoint itself. Checkpoint facts come from the weight repository at the resolved commit. Entries of kind: draft describe speculative-decoding drafts and are not served directly. The entry directory name is the launch name: The schema is documented in the launcher repository.
Modelos de TTS neural local para o LylyReader (app Android de audiobooks, repo JonJonesBR/LylyReader-Android). Infraestrutura de modelo do projeto num lugar só — hospedar aqui evita depender de repositórios de terceiros que podem cair ou ser removidos. Por que FP32 (2026-09-03): o bundle INT8 (kokoro-int8-multi-lang-v10.zip, ainda hospedado como fallback/arquivo) tem artefato auditivo de sibilo/chiado agudo (ressalto ~9,6kHz + ruído de quantização — validado por ouvido contra o FP32, que é limpo). O app baixa o FP32. filesDir/kokoro/ (mesmo mecanismo para os bundles da Parte A). Fonte original: release tts-models do k2-fsa/sherpa-onnx. Licença Apache-2.0. mms-tts-por-v1.zip — voz VITS pt-BR…
Matchanu 1.5 is PyGrassReal's generative 3D asset engine capable of synthesizing production-ready watertight 3D meshes (STL, OBJ, PLY) and real-time 3D Gaussian Splats from text descriptions or single 2D concept images. Designed for Industrial Prototyping (3D Printing), Game & AR/VR Assets, and Architectural Elements, Matchanu 1.5 generates clean topology, UV unwrappings, and volume-accurate meshes without the point cloud artifacts common to open-source prototypes. 1. Prompt & Image-to-3D Mesh: - Converts single-view product photos or text prompts into clean, manifold 3D meshes ready for Blender, Rhino, Maya, or Unreal Engine. 2. 3D Printing Ready (Watertight Topology): - Automatically…
A domain-specific small language model for step-by-step math problem solving, built by team03 (SLM Learners) for the Pramana SLM++ Bootcamp Round 2 submission. For an OpenAI-compatible endpoint, serve with servehf.py (stdlib + transformers only, no Ollama needed). Precision note: training ran in bf16 compute (QLoRA 4-bit NF4 base), but the merged checkpoint uploaded here is float16 (the merge step reloads the base in fp16). - Public Hugging Face datasets pulled via pulldata.py; licenses verified through the HF API on 2026-09-05 and recorded in datamanifest.md. - Held-out eval set built with buildheldouteval.py from raw ExamBench rows never used in training, with a final overlap check that…
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.