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.
12,283Models
3,510Datasets
290Papers
7,436Publishers
12,632Sourced relationships
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Ru-Small-Instruct — экспериментальная компактная русскоязычная языковая модель класса SLM (Small Language Model) с объемом параметров ~0.2B (~165M). Разработана с упором на суверенность весов (Zero-Fingerprint): модель обучена с нуля без заимствования базовых чекпоинтов у сторонних корпоративных сетей (Llama 3 от Meta, Qwen от Alibaba, Mistral). Модель предназначена для исследований локального инференса, работы на маломощном оборудовании, CPU и мобильных чипах, где критичны нулевая задержка (Time-To-First-Token) и полная независимость весов. Для компактной модели в 165M параметров, обученной на одном домашнем GPU за 48 часов, способность держать роль, грамотно формулировать сложные термины…
RubriX is a non-autoregressive rubric judge. Given a criterion, the rubric's level descriptions and a response (optionally with the conversation it answers), it returns a probability for every level in a single pass, with no text generation. Level descriptions are embedded once per rubric and cached, so the cost of each judgment does not grow with the number or length of the levels. Read more in the blog post: Introducing RubriX. One encoder, Qwen3-8B with this repository's LoRA adapter, embeds the input and each level description separately. Two projection heads map them into a shared space, and - sk = τ · cos(f(input), g(levelk)), with τ = 100 - p = softmax(s1 … sK) The model was trained…
This model is a LoRA finetuned Qwen model that employs production rules to ensure strictt rule compliance with Grade2 Braille rules and outperforms a neural-only model on UEB compliance metrics like compliance, capitalization, punctuation, numeric, and OOV accuracy. These individual metrics form the model's recall. The precision score of the model (accounts for character insertions in the outputs differing from references) is also way better than the stand-alone neural model.
Национальный ИИ от DICPlay. Самая лёгкая модель семейства RusMind — работает прямо на вашем устройстве, без интернета. Моментум — облегчённая модель семейства RusMind 3.2, созданная специально для смартфонов и нетребовательных компьютеров. Она отвечает быстро, работает без подключения к сети, и ни одно слово разговора не покидает устройство. RusMind обучалась на огромном массиве данных, а затем прошла специальное дообучение на русскоязычном корпусе. В него вошли: Приложения с GGUF (LM Studio, Ollama, PocketPal и другие) и llama.cpp — берите целый файл: Файлы rusmind-momentum-3.2-000NN-of-00009.gguf — та же модель, разрезанная на части до 1 ГБ для загрузки в браузер на rusmind.ru. Все…
Национальный ИИ от DICPlay. Модель для повседневных задач, которая работает прямо на вашем устройстве — без интернета. Вспышка — модель семейства RusMind 3.2 для повседневных задач: письма, тексты, учёба, объяснения. Она рассчитана на мощные компьютеры и флагманские смартфоны, работает без подключения к сети, и ни одно слово разговора не покидает устройство. RusMind обучалась на огромном массиве данных, а затем прошла специальное дообучение на русскоязычном корпусе. В него вошли: Приложения с GGUF (LM Studio, Ollama, PocketPal и другие) и llama.cpp — берите целый файл: Файлы rusmind-vspyshka-3.2-000NN-of-00014.gguf — та же модель, разрезанная на части до 1 ГБ для загрузки в браузер на…
Национальный ИИ от DICPlay. Модель на устройстве для повседневных задач: пишет, объясняет, помогает с учёбой и кодом. Работает без интернета на мощных ПК и флагманских телефонах. LM Studio, Ollama, PocketPal и другие программы с GGUF — скачайте нужный файл и откройте его. RusMind обучалась на огромном массиве данных и прошла дообучение на русскоязычных наборах DICPlay: история и культура России, законы, регионы и народы страны, традиционные семейные ценности, ключевые события — на русском и английском. Распространяется по собственной лицензии RusMind License (DICPlay) — текст в файле LICENSE. Коротко: бесплатно для личного и некоммерческого использования, исследований и оценки; коммерческое…
Part of the Standalone Models by Convergent Intelligence LLC: Research Division This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts: For the full mathematical treatment, see Discrepancy Calculus: Foundations and Core…
Converted with mlx-lm 0.31.3. A six-case deterministic sanity check matched three upstream reference strings exactly. The remaining differences included a retained leading “So,” punctuation/ordinal variation, and omission of “tomorrow” in one correction case. This is a small functional check, not the upstream 7,519-case evaluation; assess the 4-bit build on your own transcripts. A 0.6B-parameter text normalizer for speech-to-text output. It takes a raw ASR transcript and rewrites it as clean written text: fillers removed, false starts and self-corrections resolved to the value the speaker landed on, punctuation and capitalization applied, and spoken numbers, dates, times, currency and email…
Converted with mlx-lm 0.31.3. A six-case deterministic sanity check matched four upstream reference strings exactly. The two differences were a retained leading “So,” and one comma variation. This is a small functional check, not the upstream 7,519-case evaluation. A 0.6B-parameter text normalizer for speech-to-text output. It takes a raw ASR transcript and rewrites it as clean written text: fillers removed, false starts and self-corrections resolved to the value the speaker landed on, punctuation and capitalization applied, and spoken numbers, dates, times, currency and email addresses rendered in written form. On a held-out set of 7,519 English cases it reaches 94.8% token accuracy, and…
Native routed LoRA weights from omnicommon400/checkpoint-200. The tensor file is copied unchanged from the requested checkpoint. Common400 are in-sample and must not be presented as held-out performance. It is separate from the paper's cubic S→R main-result checkpoints; the paper's 6.85% CER and 4.51 MOS are not asserted for this artifact. Use Python 3.12 with a compatible PyTorch installation. Install the upstream OmniVoice 0.1.5 inference package and Hugging Face client: Replace the text and reference inputs with your own. The reference transcript must match the supplied recording. Use --device cpu if CUDA is unavailable. Use --base-dir /path/to/OmniVoice to reuse a local base-model…
We have completely redesigned SACHITRA AI Studio 3.0 with a state-of-the-art Deep Void Cyber Studio UI, animated mesh background, glowing glassmorphic panels, visual style tiles, interactive aspect ratio matrix, and 1-click magic prompt enhancer. - Deep Void Background (#030509): Layered with an animated mesh grid overlay and multi-color radial gradient aura. - Real-time CPU telemetry cards ($3.28\times$ AFDT speedup, $24.2\text{ MB}$ 3D-CHAD INT4 attention RAM savings, $4.9\text{ GFLOPS}$ WASM SIMD throughput). - 16/16 Automated Unit Tests Passing (python -m unittest discover tests). Below is the full browser recording demonstrating the redesigned Deep Void Cyber Studio UI, Magic Prompt…
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 repository for SAM 2++: Tracking Anything at Any Granularity, a unified video tracking framework that extends the SAM 2 model to track any targets in videos at any granularity, including masks, bounding boxes, and points. See the SAM 2++ paper for more information. Models can alternatively be loaded from Hugging Face
Mirror of the original SCUNet checkpoint scunetcolorrealgan.pth, unchanged. Used by the ComfyUI node Rennart Denoise (ComfyUI-Rennart), which downloads it automatically. Loads in ComfyUI with the standard Load Upscale Model node (spandrel). Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis, Machine Intelligence Research, 2023. MIT — Copyright (c) Kai Zhang. The original license text: https://github.com/cszn/KAIR/blob/master/LICENSE
Mirror of the original SCUNet checkpoint scunetcolorrealpsnr.pth, unchanged. Used by the ComfyUI node Rennart Denoise (ComfyUI-Rennart), which downloads it automatically. Loads in ComfyUI with the standard Load Upscale Model node (spandrel). Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis, Machine Intelligence Research, 2023. MIT — Copyright (c) Kai Zhang. The original license text: https://github.com/cszn/KAIR/blob/master/LICENSE
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.