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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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
static quants of https://huggingface.co/dancingfrog/Nezou-27B-CyberAbliterated For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nezou-27B-CyberAbliterated-i1-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…
weighted/imatrix quants of https://huggingface.co/dancingfrog/Nezou-27B-CyberAbliterated For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Nezou-27B-CyberAbliterated-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…
This repository mirrors the public engine/ directory from GitHub commit 2a1174cc5f82. It contains the Python formulation algorithms, 22 plaintext JSON nutrition tables, and ingredient/rule JSON resources. No desktop installer or encrypted DataPack is included. Start with engine/README.md for file locations and Python runtime instructions. Licensing: Python source is Apache-2.0. Nutrition data and JSON resources remain PalEcho copyrighted under DATALICENSE.md, including attribution and redistribution limits. The data files are not Apache-2.0 licensed. The included dataset reports productionready: false and buildmode: REVIEWONLY.
weighted/imatrix quants of https://huggingface.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/North-Mini-Code-1.0-Uncensored-Heretic-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…
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). - PEFT 0.19.1
Open-source inference and training code that regresses a relative-elevation DEM directly from a single optical remote-sensing image (orthorectified RGB). The backbone is the encoder of Depth-Anything-3 (da3-base, ViT-B/DINOv2) with a dual DPT depth head, fine-tuned end-to-end on remote-sensing DEM tiles, plus a lightweight flatness detection head (TinyFlathead) attached for flat-region regularisation. The weights released here correspond to the configuration selected in the paper: Neither script contains an absolute path, and neither depends on code or weights outside the package. Every resource is derived from the script's own file and resolved against the package root (opensource/), so…
This model is a fine-tuned version of allenai/OLMo-2-1124-7B-Instruct. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of allenai/OLMo-2-1124-7B-Instruct on the Neelectric/wildguardmixLlama-3.1-8B-Instruct4096toks dataset. It has been trained using TRL. This model was trained with SFT.
Built with Qwen. Non-commercial research/evaluation only under the included Qwen RESEARCH LICENSE AGREEMENT. These modified native checkpoints use the pinned base revision aa8e72537993ba99e69dfaafa59ed015b17504d1. See MODIFICATIONS.md and each checkpoint manifest. Complete persisted student, teacher, optimizer, RNG, scheduler and saved loader state; portable LoRA is stored separately in zcheng256/OPSD-SDPO-v22-Qwen2.5-3B-LoRA. Checkpoint integrity does not imply efficacy or tested fresh-process vLLM sampling resume. Train seed42 and eval seed1001 only. Uploads never block training/evaluation. No questions, answers, reference reasoning, rollouts, logs or credentials are published.…
SurgHint is a surgical visual question answering system developed for the FRAME and SEGMENT tracks of the ORena FOCUS Challenge. This repository provides the detector checkpoints and Qwen LoRA adapters for answering questions about foreign objects in surgical images and video clips. VLMMAXXING — Orhun Utku Aydin, Frank te Nijenhuis, Dietmar Frey. Sign in and request access on this model page to download the checkpoints. Qwen base weights and FRAME's frozen DINOv3 backbone are not included; please obtain them separately under their upstream licences and access terms. SurgHint inference uses greedy decoding (dosample=False) with thinking disabled. The inference code overrides the sampling…
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.