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 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
/ Import a cleaner font / / Light Mode Palette / / Dark Mode Palette - High Contrast Neon / --bg-pink-light: #1a050d; / Deep burgundy / --bg-pink-dark: #0a0205; / Near black / --glass-bg: rgba(30, 5, 15, 0.6); / More transparent for neon effect / --glass-border: rgba(255, 105, 180, 0.5); / Brighter border / --text-main: #ffb6c1; / Light pink text / / Main Container - Glassmorphism Effect / / Animated Border Glow / background: linear-gradient(45deg, var(--primary-pink), transparent, var(--secondary-pink), transparent) border-box; / Header Section / / Waifu Image Container / / Content Sections / / List Styling / / Disclaimer Section / / Badge Styling / / Fix for Hugging Face's own dark mode…
Ask anything, get a how-to article. WikiQwen-4B is Qwen3.5-4B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…
Ask anything, get a how-to article. WikiQwen-9B is Qwen3.5-9B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…
Give it a caption, get a step illustration. WikiQwen-Illustrator is a style LoRA for [FLUX.2 [klein] base 4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B). It turns a one-to-three-sentence scene description into a clean, friendly how-to illustration at 512x384. It is the picture half of WikiQwen: the chat models (4B, 9B, 35B-A3B) write an [IMAGE: caption] line above every step, and this LoRA draws it. Inside a WikiQwen article, a step looks like this. The first line is the Illustrator's prompt, and the second is the step it illustrates: - No trigger word. The training captions describe content only, with no style words, so the style comes along automatically. It takes a…
One blend of Qwen3.8-Flash-Next (125B MoE, 512 experts, 10 active) in three bottles, sized so it runs on a gaming PC with Winery Strata. That's a fork of Strata with a wine-red glass UI, where Cuvée is the first model in the installer. - Short thinking from Swift 1.5 (UkisAI) - Refusals poured out by Huihui's abliteration Visit the Cuvée Space for the expert-cellar map and a bottle picker for your RAM. All three keep the dense weights in Q6K/Q80 and the 28.8 GB PLE table in IQ4NL (Strata reads it from the SSD). They were quantised with Unsloth's imatrix for Flash-Next. Slim drops half the experts, the same move as ISTA-DASLab's Coder. The difference is that it keeps the 256 experts routed…
Jieyuan Pei, Meiyi Lu, Sining Ang, Yubo Zhao, Zhangyi Hu, Mingwei Xu, Haokai Ding, Wei Li, Zihan You, Jianwei Zheng, Li Yu, Yifeng Pan, Ji Tao, Rongjunchen Zhang, Yan Wang Checkpoints of World4Scorer (paper, code). World4Scorer makes the candidate scorer of a generate-and-select planner a trajectory-conditioned latent world model. One shared predictor gives every candidate trajectory a predicted state, and score heads read its outcomes (collision, drivable area, progress, time to collision, comfort) from that state. Simulator outcomes supervise every candidate; the one future the driving log actually recorded anchors the shared predictor during training. No future is needed at test time.…
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.