# Models, Page 447 of 464
Source: https://savrn.com/models/page/447
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,362](https://savrn.com/models?task=Text+generation)
- [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction332](https://savrn.com/models?task=Feature+extraction)
- [Robotics329](https://savrn.com/models?task=Robotics)
- [Text classification293](https://savrn.com/models?task=Text+classification)
- [Text to image202](https://savrn.com/models?task=Text+to+image)
- [Speech recognition170](https://savrn.com/models?task=Speech+recognition)
- [Audio classification157](https://savrn.com/models?task=Audio+classification)
- [Image classification135](https://savrn.com/models?task=Image+classification)
- [Text to speech130](https://savrn.com/models?task=Text+to+speech)
- [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity)
- [Token classification96](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification)
- [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning)
- [Image segmentation87](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Translation77](https://savrn.com/models?task=Translation)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask67](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification59](https://savrn.com/models?task=Video+classification)
- [Image to text57](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,718](https://savrn.com/models?library=transformers)
- [peft329](https://savrn.com/models?library=peft)
- [diffusers183](https://savrn.com/models?library=diffusers)
- [pytorch169](https://savrn.com/models?library=pytorch)
- [gguf165](https://savrn.com/models?library=gguf)
- [sentence-transformers138](https://savrn.com/models?library=sentence-transformers)
- [mlx136](https://savrn.com/models?library=mlx)
- [lerobot135](https://savrn.com/models?library=lerobot)
- [timm49](https://savrn.com/models?library=timm)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [llama.cpp40](https://savrn.com/models?library=llama.cpp)
- [vllm38](https://savrn.com/models?library=vllm)
- [onnx35](https://savrn.com/models?library=onnx)
- [diffusion-single-file27](https://savrn.com/models?library=diffusion-single-file)
- [minimax-h324](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [openvino18](https://savrn.com/models?library=openvino)
- [coreml17](https://savrn.com/models?library=coreml)
- [openpi16](https://savrn.com/models?library=openpi)
- [exllamav315](https://savrn.com/models?library=exllamav3)
- [stable-baselines315](https://savrn.com/models?library=stable-baselines3)
- [keras14](https://savrn.com/models?library=keras)

Modality

- [Other6,276](https://savrn.com/models?modality=Other)
- [Text2,536](https://savrn.com/models?modality=Text)
- [Image554](https://savrn.com/models?modality=Image)
- [Image and text551](https://savrn.com/models?modality=Image+and+text)
- [Audio473](https://savrn.com/models?modality=Audio)
- [Control420](https://savrn.com/models?modality=Control)
- [Video114](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular55](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,031](https://savrn.com/models?license=apache-2.0)
- [mit1,221](https://savrn.com/models?license=mit)
- [other681](https://savrn.com/models?license=other)
- [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause)
- [gemma59](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.234](https://savrn.com/models?license=llama3.2)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.127](https://savrn.com/models?license=llama3.1)
- [unknown26](https://savrn.com/models?license=unknown)
- [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [gpl-3.016](https://savrn.com/models?license=gpl-3.0)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail13](https://savrn.com/models?license=openrail)
- [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m)
- [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [llama26](https://savrn.com/models?license=llama2)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights10,787](https://savrn.com/models?access=open_access)
- [Access requested at publisher335](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai)
- [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr)
- [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Jessen Pu69](https://savrn.com/models?publisher=putty77)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr)
- [Firza Hadzami60](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [Microsoft48](https://savrn.com/models?publisher=microsoft)
- [NVIDIA46](https://savrn.com/models?publisher=nvidia)
- [Khazzz1c44](https://savrn.com/models?publisher=khazic)
- [Quan44](https://savrn.com/models?publisher=wq2012)
- [Yurun Yuan44](https://savrn.com/models?publisher=yuruny)
- [BAAR43](https://savrn.com/models?publisher=aixk)
- [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud)

[Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params)

11,122 models, sorted by most downloaded.

Model

### [train_800_dense__no_mask__pi0__seed_0](https://savrn.com/models/train-800-dense-no-mask-pi0-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-dense-no-mask-pi0-seed-0)

Model

### [train_800_dense__point__overlay_a100__sim__all_cameras__live__pi05__seed_0](https://savrn.com/models/train-800-dense-point-overlay-a100-sim-all-cameras-live-pi05-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-dense-point-overlay-a100-sim-all-cameras-live-pi05-seed-0)

Model

### [train_800_dense__point__overlay_a75__sim__all_cameras__live__pi05__seed_0](https://savrn.com/models/train-800-dense-point-overlay-a75-sim-all-cameras-live-pi05-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-dense-point-overlay-a75-sim-all-cameras-live-pi05-seed-0)

Model

### [train_800_dense__point__overlay_a75__sim__all_cameras__live__pi0__seed_0](https://savrn.com/models/train-800-dense-point-overlay-a75-sim-all-cameras-live-pi0-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-dense-point-overlay-a75-sim-all-cameras-live-pi0-seed-0)

Model

### [train_800_sparse__mask__overlay_a75__sim__all_cameras__live__pi0__seed_0](https://savrn.com/models/train-800-sparse-mask-overlay-a75-sim-all-cameras-live-pi0-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-sparse-mask-overlay-a75-sim-all-cameras-live-pi0-seed-0)

Model · Robotics

### [train_800_sparse__mask__overlay_a75__sim__wrist_cameras__live__pi05__seed_0](https://savrn.com/models/train-800-sparse-mask-overlay-a75-sim-wrist-cameras-live-pi05-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

Open weights apache-2.0 4.1B parameters lerobot

[View model](https://savrn.com/models/train-800-sparse-mask-overlay-a75-sim-wrist-cameras-live-pi05-seed-0)

Model

### [train_800_sparse__mask_a75__ur5e__pi05__seed_0__ab_b30](https://savrn.com/models/train-800-sparse-mask-a75-ur5e-pi05-seed-0-ab-b30)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

Open weights

[View model](https://savrn.com/models/train-800-sparse-mask-a75-ur5e-pi05-seed-0-ab-b30)

Model · Robotics

### [train_800_sparse__no_mask__ur5e__pi05__seed_0__b_b100](https://savrn.com/models/train-800-sparse-no-mask-ur5e-pi05-seed-0-b-b100)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

Open weights apache-2.0 4.1B parameters lerobot

[View model](https://savrn.com/models/train-800-sparse-no-mask-ur5e-pi05-seed-0-b-b100)

Model · Robotics

### [train_800_sparse__no_mask__ur5e__pi05__seed_0__b_b30](https://savrn.com/models/train-800-sparse-no-mask-ur5e-pi05-seed-0-b-b30)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

Open weights apache-2.0 4.1B parameters lerobot

[View model](https://savrn.com/models/train-800-sparse-no-mask-ur5e-pi05-seed-0-b-b30)

Model · Robotics

### [train_800_sparse__point__overlay_a75__sim__all_cameras__live__pi0__seed_0](https://savrn.com/models/train-800-sparse-point-overlay-a75-sim-all-cameras-live-pi0-seed-0)

[Mim Chess Vlas](https://savrn.com/model-publishers/mim-chess-vlas)

π₀ (Pi0) is a general-purpose robot foundation model from Physical Intelligence: a generalist Vision-Language-Action policy that understands visual inputs, interprets natural language instructions, and controls a variety of different robots across diverse tasks. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi0 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package.…

Open weights apache-2.0 4B parameters lerobot

[View model](https://savrn.com/models/train-800-sparse-point-overlay-a75-sim-all-cameras-live-pi0-seed-0)

Model

### [train_chatterbox](https://savrn.com/models/train-chatterbox)

[Bibek](https://savrn.com/model-publishers/ibibek)

A modular, highly efficient infrastructure for fine-tuning both Chatterbox TTS (Standart) and Chatterbox Turbo models with your own dataset and generating high-quality speech synthesis. This kit is specially designed to support new languages and voices by intelligently extending the model's vocabulary. With the newly added LoRA (Low-Rank Adaptation) support, you can now train high-quality voices faster and with significantly less VRAM. This repository allows you to choose your training strategy using the islora flag in src/config.py. What is it? LoRA (Low-Rank Adaptation) freezes the massive base model and only trains tiny adapter layers alongside the new language embeddings. What is it?…

Open weights

[View model](https://savrn.com/models/train-chatterbox)

Model

### [train_progress](https://savrn.com/models/train-progress)

[SupraLabs](https://savrn.com/model-publishers/supralabs)

This repository is meant to be containing checkpoints and training progress of a new 13M parameter model, Supra2.5-Pro. It is part of our new Supra3 research program and will be share publicly soon. Please do not request access to this model before it's being released - we will not approve any requests. October 10-12 th, 2026

Access requested at publisher apache-2.0

[View model](https://savrn.com/models/train-progress)

Model

### [train_script](https://savrn.com/models/train-script)

[Maginion](https://savrn.com/model-publishers/maginion)

Open weights

[View model](https://savrn.com/models/train-script)

Model

### [trained-loras](https://savrn.com/models/trained-loras)

[Groot Johnson](https://savrn.com/model-publishers/iamgroot1212)

My trained LoRa repository. + Trigger words are found in the.md file Disclaimer: The LoRAs are provided for entertainment purposes only. You are solely responsible for ensuring that any images you generate and distribute comply with all applicable laws, including copyright, trademark, and personality rights. Do not use these LoRAs to create illegal, harmful, or non-consensual content.

Open weights apache-2.0

[View model](https://savrn.com/models/trained-loras)

Model

### [trained_brats23_models_3](https://savrn.com/models/trained-brats23-models-3)

[Es Ta](https://savrn.com/model-publishers/obi77)

Open weights

[View model](https://savrn.com/models/trained-brats23-models-3)

Model · Text generation

### [training-test-921-1](https://savrn.com/models/training-test-921-1)

[Master Super](https://savrn.com/model-publishers/master103525)

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

Open weights peft

[View model](https://savrn.com/models/training-test-921-1)

Model · Speech recognition

### [trans-local-speech-models](https://savrn.com/models/trans-local-speech-models)

[Chenggang Chen](https://savrn.com/model-publishers/aladdinchen)

Versioned, data-only runtime packages for local speech recognition in Trans. These are conversions of the credited upstream models, not models trained by AladdinChen. The original authors retain their rights. No endorsement is implied. Each package is downloaded separately. Recognition runs on the user's device; this repository contains no hosted inference service, recordings, training data, app credentials, or application code. See LICENSES.md for source authors, licenses and modification notices. Full license texts and preserved upstream notices are in licenses/ and provenance/. Redistributors must retain the notices and meet the applicable per-model license conditions. This is not a…

Open weights other

[View model](https://savrn.com/models/trans-local-speech-models)

Model · Tabular classification

### [transfer-fraud-model](https://savrn.com/models/transfer-fraud-model)

[Youssef Mlaouhia](https://savrn.com/model-publishers/yssfmlha)

Gradient-boosted classifier. You compute the 15 features yourself and pass them in; the model returns P(fraud) and a risk level 0-3. Trained on a synthetic dataset - validate on real data. predict accepts one dict, a list of dicts, or a pandas DataFrame with these columns (extra columns are ignored, order doesn't matter, missing ones raise an error). "History" = the sender's transfers strictly before the one being scored, within the last 180 days. "rocauctest": 0.9924, "prauctest": 0.8382, "fraudratetest": 0.0169, "alertprecisionlevel2plus": 0.64, "alertrecalllevel2plus": 0.8287, "recalllevel1plus": 0.9655

Open weights mit sklearn

[View model](https://savrn.com/models/transfer-fraud-model)

Model · Translation

### [transformer-en-zh](https://savrn.com/models/transformer-en-zh)

[Chou Lucas](https://savrn.com/model-publishers/chou-lucas)

基于标准 Transformer（Post-LN + 固定 sin-cos 位置编码）的英译中模型， 由本工程用 HuggingFace Seq2SeqTrainer 训练得到（checkpoint：trained with Seq2SeqTrainer (standard HF)）。 本仓库是标准 HF 仓库：包含 config.json + automap，代码随仓库分发， 因此任何人在 HF 上 trustremotecode=True 即可直接加载使用，无需本工程的其它文件。 逐步自回归推理，未实现 KV Cache（usecache=false），长句偏慢。 生成质量以 beam search（numbeams=3）优于贪心。 请按自身场景补充 LICENSE。

Open weights 98M parameters 5,000 tokens transformers

[View model](https://savrn.com/models/transformer-en-zh)

Model

### [transition](https://savrn.com/models/transition)

[Veason-silverbullet](https://savrn.com/model-publishers/veason)

Open weights

[View model](https://savrn.com/models/transition)

Model

### [transport-12256fcd9dc0](https://savrn.com/models/transport-12256fcd9dc0)

[Paul Vines](https://savrn.com/model-publishers/vines26)

Open weights

[View model](https://savrn.com/models/transport-12256fcd9dc0)

Model

### [transport-262af474f626](https://savrn.com/models/transport-262af474f626)

[Paul Vines](https://savrn.com/model-publishers/vines26)

Open weights

[View model](https://savrn.com/models/transport-262af474f626)

Model

### [transport-34eedb9f1c65](https://savrn.com/models/transport-34eedb9f1c65)

[Paul Vines](https://savrn.com/model-publishers/vines26)

Open weights

[View model](https://savrn.com/models/transport-34eedb9f1c65)

Model

### [transport-4b2b64c00788](https://savrn.com/models/transport-4b2b64c00788)

[Paul Vines](https://savrn.com/model-publishers/vines26)

Open weights

[View model](https://savrn.com/models/transport-4b2b64c00788)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-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.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## 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](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.