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.
LoRA adapters that let one open-weight model serve every LLM call of an end-to-end OpenFOAM agent: planning the case, writing the configuration files, diagnosing solver errors, and repairing the case. They accompany the paper FoamGPT: Fine-Tuning Large Language Models for Agentic Automation of CFD Simulations with OpenFOAM (anonymous submission). Code, data, and evaluation scripts are in the companion code repository. The shared adapters are the systems reported as FoamGPT. Records are teacher completions from variants 1-7 of the FoamBench Basic families; records longer than 16,384 tokens after chat templating are dropped (147 erroranalysis records with the Nemotron tokenizer, 148 with the…
These are the weights foldserve runs, each model's own checkpoint converted once into foldserve's parameter layout. The values are the original ones in fp32, only rearranged (projections fused or split, tensors renamed and reordered). They are unofficial conversions. The models belong to their authors, who do not endorse foldserve. foldserve fetch downloads a model's folder and checks every file against its pinned sha256. foldserve fetch --from-upstream downloads the authors' checkpoint instead and converts it locally, with the same result (weights.json records the checkpoint's sha256 and the parameters' digest). Each folder holds its upstream's license text (LICENSE), and its weights stay…
Modèle ASR complet affiné pour transcrire le fongbe à partir de la parole. Base: facebook/mms-1b-all; tokenizer: Fon v0.4.1. Le bundle préparé contient les poids complets sélectionnés, leur configuration, l’espace de sortie, les artefacts du tokenizer et le runtime autonome. Arrêt définitif demandé par l’utilisateur après 19 246 updates au huitième epoch. Le meilleur checkpoint retenu sur le dev d’origine est le step 18 904. Le plafond prévu était de 54 016 updates et 20 epochs. Ancienne cohorte v07: 113 320 lignes d’entraînement, 243,8558 heures réellement décodées; dev de 1 381 audios. La provenance et les empreintes de cette cohorte restent celles du contrat d’origine. Les poids n’ont…
Modèle ASR complet affiné pour transcrire le fongbe à partir de la parole. Base: facebook/mms-1b-all; tokenizer: reference. Le bundle préparé contient les poids complets sélectionnés, leur configuration, l’espace de sortie, les artefacts du tokenizer et le runtime autonome. Arrêt définitif demandé par l’utilisateur après 20 774 updates au huitième epoch. Le meilleur checkpoint retenu sur le dev d’origine est le step 20 000. Le plafond prévu était de 54 016 updates et 20 epochs. Ancienne cohorte v07: 113 320 lignes d’entraînement, 243,8558 heures réellement décodées; dev de 1 381 audios. La provenance et les empreintes de cette cohorte restent celles du contrat d’origine. Les poids n’ont pas…
Modèle ASR complet affiné pour transcrire le fongbe à partir de la parole. Base: omniASRLLM1Bv2; tokenizer: Fon v0.4.1. Le bundle préparé contient les poids complets sélectionnés, leur configuration, l’espace de sortie, les artefacts du tokenizer et le runtime autonome. Arrêt anticipé sur la validation après 8 000 updates, au septième epoch. Plafond: 54 016 updates et 20 epochs; horizon calculé sur ce train: 23 607 updates. Le checkpoint retenu est celui du step 4 721, choisi sur le CER exact du dev (WER en cas d’égalité). Split privé JaimeMaLangue/fon-meilleur-asr:split-v2.1, révision e105be46cca8784e3b89142b09fae555d905e5e8. Train: 50 323 énoncés, 106,0355 heures réellement décodées, 612…
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1
This is a pretrained foveated vision model from the fovi library. - Cortical Magnification Function Hyperparameter (a): 2.78 Apache 2.0
Public backup of pi0.5 fine-tuning checkpoints for Franka pick-and-place. Backups are in progress until backupstatus.json reports completion. Each committed checkpoint is checked against Hugging Face file sizes and content hashes. params/ and assets/ support inference. Checkpoints that also contain trainstate/ preserve optimizer and training state for resuming with the matching OpenPI implementation. Directory 19999 represents 20,000 completed updates and 24999 represents 25,000. Intermediate folder names follow the training code's zero-based step indexing. An inference-only checkpoint cannot reproduce full optimizer state. Duplicate local exports are not stored twice. Use manifests/ for…
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct. It has been trained using TRL. This model was trained with SFT.
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.