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.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
A LoRA adapter for Qwen/Qwen3.5-4B that makes typed decisions about a document in one forward pass and returns calibrated probabilities over the options: a System-1 decision engine, fast judgment rather than generated reasoning. Served with the code at github.com/tuantong/loupe-serving, which reads the answer from the next-token probabilities of the option labels; nothing is generated. An exact average (model soup) of LoRA adapters on the attention, linear-attention and MLP projections of all 32 layers (three seeds of one recipe, a continuation of that recipe on written rationales, and a run whose targets include a second teacher), stored as one rank-96 adapter. Merge it into the base…
Somente para testes. Sem resultados conclusivos. Arquivos de uma bancada em andamento; podem ser apagados ou trocados a qualquer momento. Não use em produção. - Testado em NVIDIA RTX PRO 6000 Blackwell (sm120), ComfyUI (master de out/2026) + comfykitchen, torch 2.11 cu130..comfyquant); camadas sem marcação ficam em bf16. NVFP4/MXFP8 precisam de Blackwell para rodar nativo. - Convertidos a partir do ltx-2.5-22b-distilled-transformer-bf16 oficial com os quantizadores do próprio ComfyUI (comfy/quantops.py); o.quant.json ao lado de cada arquivo descreve a receita. - Derivado de Lightricks/LTX-2.5: mesma licença do original (arquivo de licença neste repositório) — leia antes de usar. Only for…
A few LoRAs I've collected over the months. Best used with rgthree's Power LoRA Loader node so you can see each LoRA's info, strength, advice, etc.
We introduce LUNA, a small, autoregressive transformer. This model aims to provide conversational-like chat without overloading the computer. This model is designed to run on small hardware, such as phones or office computers. Luna utilizes FlashAttention and AdamW for performance and capability. Lightning is designed to provide quick, coherent outputs, improved with a larger size and weight. Luna is intended to be used for research, analysis and fine-tuning, general conversation, stories and other. It is not intended to be used for professional advice, real writing or any kind of heavy work as generated outputs may be incorrect. Luna can be used directly for text generation…
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.