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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
This is the model card of a transformers model that has been pushed on the Hub. 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).
This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - multidatasetbatchsampler: roundrobin - dopredict: False - predictionlossonly: True - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - gradientaccumulationsteps: 1 - evalaccumulationsteps: None…
This repository contains LoRA adapters produced by the TREA-ORCA audio question-answering fine-tuning runs. The available model names are af3, gemma4, and qwen2.5omni. The formats are mcq and opentext. Each task directory contains the final adapter checkpoint files: - adaptermodel.safetensors - adapterconfig.json - trainingargs.bin - trainerstate.json These are PEFT LoRA adapters, not standalone merged models. Load the matching base model first, then attach the adapter from the desired task directory with PEFT. MCQ adapters were trained with question text, four options, and a target option letter. The source data uses the question, optionA, optionB, optionC, optionD, and correct fields.…
Official model checkpoints and speaker verification voiceprints for Firebot, an autonomous tactical firefighting robot platform with dual-tier offline voice intent recognition and personalized acoustic adaptation. The Firebot voice system is engineered for zero-latency, high-reliability command recognition in noisy operational environments. It combines: 1. Frozen Whisper Encoder Feature Extraction: 384-dimensional pooled audio embeddings from OpenAI's Whisper model (tiny.en / base.en). 2. L2-SP Regularized MLP Intent Classifier: A lightweight Multi-Layer Perceptron (LayerNorm $\to$ Linear 384$\times$128 $\to$ GELU $\to$ Dropout $\to$ Linear 128$\times$15) fine-tuned with an L2 penalty…
Multilingual voice cloning on Apple audio This repository brings mlx-speech as a complete MLX voice-cloning pipeline. Give it a reference recording, its transcript, and the text you want spoken. It returns a waveform in the reference speaker's voice. App Automaton maintains the MLX conversion and runtime. Speech generation runs locally on your Mac, including speaker conditioning and waveform reconstruction. The current release is Base BF16, stored in base/mlx-bf16/. Each model variant has its own complete inference bundle. Instruct will be added under instruct/ after its MLX pipeline is validated, keeping the Base path stable. Requires an Apple Silicon Mac and Python 3.13 or later. The…
A research-oriented Flamingo prototype targeting Classification. The included giant setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a constant warmup schedule. These are starting values in the script, not evidence of a completed…
This repository is a compact, custom PyTorch implementation of Flamingo for Classification. The base configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a exponential schedule. These…
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 0.20.0
LoRA adapters (r=64, alpha=128, 12 attention/FF projections) trained with Flow-Map GRPO on the T2I-Distill MeanFlow (flux1litemeanflow.pt) and sCM (flux1litescm.pt) students of Freepik/flux.1-lite-8B, plus the online-SFT / Diffusion-NFT baselines. Code: ZhiqiLi-CG/flowmapgrpo (branch and commit in each run folder's README). Layout: checkpoints/ / / [- ]/checkpoint- /{lora, loraema, trainingstate}. lora/ is the raw learner, loraema/ the EMA adapter used for all reported numbers, trainingstate/ the full resume point. Run folders carry a README.md and the training-time fullevalmetrics.jsonl where available. Evaluation protocol for the GRPO runs: K = sampling steps - 1 (K=4 is the 5-step…
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.