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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.

11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships

Updated 2026-10-18 · How the library is built

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11,122 models, sorted by most downloaded.

Model · Image to image

zen-image-edit-SDNQ-uint4-f16

GuiAworld

Qwen-Image-2.1 on a 0.8B text encoder. Text-to-image, character and scene editing, and transparent (RGBA) generation in one pipeline. The text encoder is replaced by Qwen3.5-0.8B plus a 158M adapter, fine-tuned to reproduce what the native encoder produced — both from plain text and from text read together with the reference images (Improved using Qwen). The adapter lives inside the DiT as its text-fusion block, so the whole model is one self-contained diffusers folder and no 17.5 GB encoder is needed anywhere. The sampler runs a plain static shift of 5.0 instead of the original dynamic shifting. The bundled adapter is revision v12: its attention-branch position table covers 2304 slots and…

Open weights other 4B parameters diffusers
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weighted/imatrix quants of https://huggingface.co/ZenithLLM/zen-ingress-1-3b-dpo-merged For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/zen-ingress-1-3b-dpo-merged-GGUF 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 here are Artefact2's thoughts on the matter…

Open weights apache-2.0 transformers
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Reading notes and an experiment sketch for Zero Shot Transfer. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs.…

Open weights cc-by-4.0 24,832 parameters 512 tokens
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About 34M parameters, trained from scratch on a laptop. Reads a question plus a chunk whose sentences are tagged [A] [B] [C]... and answers [C] or, so it never retypes (or invents) text. best.pt is a PyTorch checkpoint for the ZeroMem code (not a transformers model); tokenizer.json is its byte-level BPE tokenizer.

Open weights
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Model Collections

Hand-picked starting points, each with the reason it exists.

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.

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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.