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 2026-10-21 · How the library is built
12,010 models, sorted by most downloaded.
Bobcat Flash 1.2 · Bobcat Flash 1.2 FP8 · Try it in the Space · Code on GitHub Bobcat Flash is the fast tier of Bobcat, a typed-decision model. You send a state (text or JSON) and questions whose possible answers you define in the request; Flash returns a probability for every answer you named, and nothing else. It never generates text: it reads the logits of the offered candidates at the first answer position of one forward pass, and the host builds a closed JSON reply from your own names. An answer can be wrong, but it cannot be malformed. This repository, sanghwa-na/bobcat-flash-1.2, holds Bobcat Flash 1.2 as ready-to-serve BF16 weights on google/gemma-4-26B-A4B-it (Apache-2.0) at…
A pre-quantized FP8 checkpoint of Bobcat Flash 1.2, the fast tier of the typed-decision model Bobcat: you send a state and questions whose answers you name, and it returns a probability for every answer you named; it never generates text. Evaluation, training data, limitations and the full license notes are on the main card, This repository, sanghwa-na/bobcat-flash-1.2-fp8, is the BF16 weights of sanghwa-na/bobcat-flash-1.2 quantized to FP8 (27.2 GB, FP8DYNAMIC, every expert included): float8e4m3fn weights with one scale per output channel, activations quantized per token at run time, in the compressed-tensors format that vLLM loads as it is. It is the checkpoint the Bobcat Flash Space…
Machine intuition. These selected gameplay recordings show Bongard-mini choosing actions from candidate lists. For board games, the engine supplies legal moves and their computed features; 2048 and Tetris also include engine ratings of moves. A short instruction states the strategy, and the model selects each action. The scores describe these selected recordings with the supplied engine features. Doom — Defend the Center The model receives a text description of the scene every 0.2 seconds and chooses turnleft, turnright or fire. Across 32 episodes capped at 30 seconds, it averaged 10.25 kills, with a best of 20. The GIF shows an excerpt, with probabilities beside the action. Bongard-mini is…
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).
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).
This is an uncensored research model. Its built-in refusals were removed, so it answers prompts the base model declines, including chemical and biological synthesis, cybercrime, weapons, harassment, and fraud. Use it for red-teaming, offensive-security research, and refusal-rate evaluation. It has no guardrails of its own: if you deploy it, add your own input and output moderation (for example Llama Guard). Abliterated variant of prism-ml/Ternary-Bonsai-2-27B-gguf (PrismML's ternary compression of Qwen/Qwen3.8-27B, 27B hybrid attention + GatedDeltaNet). The refusal direction was removed with runtime projective steering (GLP, the weightless method behind the Inkling steering vectors)…
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.