This model was quantized using oQ (oMLX v0.6.3rc3) mixed-precision quantization.
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.
12,283Models
3,510Datasets
290Papers
7,436Publishers
12,632Sourced relationships
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
This model was quantized using oQ (oMLX v0.7.1.dev1) mixed-precision quantization.
This repository is an Ollama deployment package for Qwen3.8 27B. It combines the jrell IQ4XS-style main quantization with a separate Q40 MTP draft model and targets a 262,144-token (256K) context window. This is a packaging and runtime-configuration project, not a new training run, fine-tune, merge, or official Qwen release. It does not claim ownership of the upstream model or quantizations. The repository contains scripts, configuration, attribution, and documentation. It does not contain the large GGUF weights. install.ps1 downloads those artifacts from their canonical upstream repositories at install time, so this repository can be renamed and updated without re-uploading multi-GB files.…
weighted/imatrix quants of https://huggingface.co/Rumiii/Qwimi-4B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Qwimi-4B-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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
static quants of https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…
static quants of https://huggingface.co/veldierin/Qwopus3.8-27B-Flash-V2-Apostate-Uncensored For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…
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 0.18.1
Artifacts of the RACE2 (transition-aware fine-tuning of pi0.5) experiments on the VLABench The original training set (5,000 episodes, PELT labels) is SeonghoonYu/vlabenchprimitiveftlerobot224. Each run folder carries its assets/ (norm stats), weights, and where available README.md, trainingconfig.json, provenance.json and logs/.
Pretrained checkpoints for REVA-PO: Stabilizing Reinforcement Learning for Chest X-ray Report Generation, accepted to ECCV 2026. REVA-PO is a reinforcement learning framework for chest X-ray report generation. It combines: - Response-Weighted Regularization, which adjusts response-level KL regularization using the response advantage and reference-policy entropy. - Validation-Anchored Policy Reset, which periodically synchronizes the current and reference policies with the best validation checkpoint. - A three-stage training pipeline consisting of warm-up training, classifier-guided supervised fine-tuning, and reinforcement learning. This repository contains the PyTorch and DeepSpeed…
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.