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 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
Repackaged model files for ComfyUI. - https://huggingface.co/MiniMaxAI/MiniMax-H3 - https://huggingface.co/lightx2v/Minimax-h3-Turbo - https://huggingface.co/alibaba-pai/MiniMax-H3-Fun-Controlnet-Union - https://huggingface.co/Kijai/MiniMax-H3-experimental The Qwen3-VL-32B nvfp4awq quant is converted from: https://huggingface.co/cybermotaz/Qwen3-VL-32B-Instruct-NVFP4 This nvfp4 text encoder does not require Blackwell GPU to use. For diffusion models prefer int8convrot if you are able to use pytorch with cu130. fp8scaled should only be used if you cannot use int8convrot. Place the files in the following folders: To use the embeddings in a text prompt, invoke them in the CLIPTextEncode node…
RVC のモデルに同梱する音声エディタです。音声を並べて、まとめて声を変換し、 1 本に書き出せます。画面は 5 か国語（日本語・English・Español・简体中文・한국어）に 上の Files and versions から Mini RVC Studio Pro.7z を取得してください。 - パスワードが必要です。 購入時にお渡しした使い方ガイド PDF の 2 ページ目に書いてあります - Mini RVC Studio Pro.exe … エディタ本体 - 使い方ガイド（EXE と同じ場所に 5 か国語） - Mini RVC Studio Pro 使い方ガイド.pdf - Mini RVC Studio Pro User Guide (English).pdf - Mini RVC Studio Pro Guía de uso (Español).pdf - Mini RVC Studio Pro 使用指南（简体中文）.pdf - Mini RVC Studio Pro 사용 가이드 (한국어).pdf - RVC 一式と実行環境（Python・PyTorch など） モデル (.pth /.index) は入っていません。 別途お渡しするモデルを assets\weights と logs に入れるか、アプリの ＋ ボタンから読み込んでください。 Windows 10 / 11（64 ビット）。NVIDIA GPU があると速く動きます。GPU が無くても使えます。 An audio editor bundled with RVC voice…
Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately.
Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately.
Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately.
Using llama.cpp release b3825 for quantization. Original model: https://huggingface.co/ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 All quants made using imatrix option with dataset from here Run them in LM Studio Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q80 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. Thanks! First, make sure you have hugginface-cli installed…
static quants of https://huggingface.co/Bedirhn77/Mithra-2B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…
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.