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 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Modèle vocal & conversationnel tunisien (Derja) quantifié au format GGUF. Compatible avec Ollama, llama.cpp, LM Studio, Jan, et mobiles (S24 Ultra).
This repository contains weights or code derived from the TurboVLA foundational architecture developed by Hugging Face and the TurboVLA Authors.
Epochs: 5; Batch size: 32; Optimizer: AdamW; Max input token length: 256.
ONNX export of dmis-lab/UltraBERT-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25…
ONNX export of dmis-lab/UltraBERT-380M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25…
ONNX export of dmis-lab/UltraBERT-Embed-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared the sentence embedding (first-token pooling, as in the sentence-transformers config) with cosine similarity. Fifteen texts are…
This is a port of UltraTex (MIT, Yibo Zhang) to the Qwen-Image 2.1 backbone, trained by Atlas 3D (orwelian84). The released UltraTex weights only cover FLUX.1-dev and FLUX.2-Klein, so this is, to our knowledge, the first Qwen-Image multi-view texture LoRA. Code: github.com/Atlas3DSS/UltraTex (fork of upstream with the Qwen-Image 2.1 and Krea 2 backbones on the default branch). The code/ folder here is a snapshot of the same files. The method is UltraTex's. This release is the backbone port (code/) plus the trained weights (qwen21/) and samples. - 15,000 steps, 1024 px per view, batch 1 - lr 5e-5, constant; bf16; seed 42 - Sparse attention top-k 0.2 - Trained on G-buffer TexVerse…
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 0.21.0
VANGUARD-MODEL-V1 Evidence-Grounded Financial Intelligence for Enterprise Applications VANGUARD-MODEL-V1 is a financial domain-adapted language model based on Qwen3-4B-Instruct-2507, fine-tuned using Quantized Low-Rank Adaptation (QLoRA) for financial question answering and document reasoning. Designed for integration into enterprise financial intelligence systems, the model aims to help applications answer questions about financial statements, annual reports, SEC filings, corporate disclosures, and financial tables using supplied evidence. The model is intended to operate within a retrieval-augmented generation (RAG) pipeline, with source retrieval, citation validation, and deterministic…
static quants of https://huggingface.co/NandiAi/VKP-Omni-2B 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 quant And…
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.