# Models, Page 385 of 477
Source: https://savrn.com/models/page/385
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.

11,442Models

3,247Datasets

285Papers

6,976Publishers

12,091Sourced relationships

Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,413](https://savrn.com/models?task=Text+generation)
- [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction341](https://savrn.com/models?task=Feature+extraction)
- [Robotics338](https://savrn.com/models?task=Robotics)
- [Text classification303](https://savrn.com/models?task=Text+classification)
- [Text to image209](https://savrn.com/models?task=Text+to+image)
- [Speech recognition171](https://savrn.com/models?task=Speech+recognition)
- [Audio classification157](https://savrn.com/models?task=Audio+classification)
- [Image classification137](https://savrn.com/models?task=Image+classification)
- [Text to speech132](https://savrn.com/models?task=Text+to+speech)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Token classification97](https://savrn.com/models?task=Token+classification)
- [Reinforcement learning93](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation88](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)
- [Translation78](https://savrn.com/models?task=Translation)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](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 text58](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,777](https://savrn.com/models?library=transformers)
- [peft345](https://savrn.com/models?library=peft)
- [diffusers187](https://savrn.com/models?library=diffusers)
- [pytorch178](https://savrn.com/models?library=pytorch)
- [gguf167](https://savrn.com/models?library=gguf)
- [lerobot139](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [mlx136](https://savrn.com/models?library=mlx)
- [timm49](https://savrn.com/models?library=timm)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [llama.cpp44](https://savrn.com/models?library=llama.cpp)
- [vllm38](https://savrn.com/models?library=vllm)
- [onnx36](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)
- [exllamav316](https://savrn.com/models?library=exllamav3)
- [openpi16](https://savrn.com/models?library=openpi)
- [litert15](https://savrn.com/models?library=litert)
- [rkllm15](https://savrn.com/models?library=rkllm)

Modality

- [Other6,473](https://savrn.com/models?modality=Other)
- [Text2,612](https://savrn.com/models?modality=Text)
- [Image and text570](https://savrn.com/models?modality=Image+and+text)
- [Image565](https://savrn.com/models?modality=Image)
- [Audio478](https://savrn.com/models?modality=Audio)
- [Control431](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)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,105](https://savrn.com/models?license=apache-2.0)
- [mit1,246](https://savrn.com/models?license=mit)
- [other703](https://savrn.com/models?license=other)
- [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause)
- [gemma62](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [unknown27](https://savrn.com/models?license=unknown)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.017](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [llama27](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 weights11,087](https://savrn.com/models?access=open_access)
- [Access requested at publisher355](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai94](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [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 Yuan63](https://savrn.com/models?publisher=yurunyyr)
- [Firza Hadzami61](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)
- [Yurun Yuan48](https://savrn.com/models?publisher=yuruny)
- [NVIDIA46](https://savrn.com/models?publisher=nvidia)
- [Khazzz1c44](https://savrn.com/models?publisher=khazic)
- [Quan44](https://savrn.com/models?publisher=wq2012)
- [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)

11,442 models, sorted by most downloaded.

Model

### [olmo3-calibration-models](https://savrn.com/models/olmo3-calibration-models)

[Edwin Huang](https://savrn.com/model-publishers/ehuang5)

DPO checkpoints from a study of why DPO makes Olmo 3 7B Instruct overconfident on multiple-choice questions. Every model here is trained from allenai/Olmo-3-7B-Instruct-SFT on Ai2's Dolci-Instruct-DPO preference pairs with Ai2's open-instruct code. - Code, results and the write-up ("Why Does DPO Make Olmo 3 Overconfident?"): These are research checkpoints for reproducing the study's measurements. They were not tuned or safety-tested for use. "Agreement-weighted" DPO multiplies each pair's loss by sigmoid(-5 x the SFT model's preference margin), rescaled to average 1, so pairs the SFT model already gets right count less. Each folder is a full model (model.safetensors, config, tokenizer, chat…

Open weights apache-2.0

[View model](https://savrn.com/models/olmo3-calibration-models)

Model

### [oloop-instruct_ablate-scales](https://savrn.com/models/oloop-instruct-ablate-scales)

[Adam Klein](https://savrn.com/model-publishers/aklein4)

Open weights

[View model](https://savrn.com/models/oloop-instruct-ablate-scales)

Model

### [oloop-instruct_baseline-fixed](https://savrn.com/models/oloop-instruct-baseline-fixed)

[Adam Klein](https://savrn.com/model-publishers/aklein4)

Open weights

[View model](https://savrn.com/models/oloop-instruct-baseline-fixed)

Model

### [oloop-instruct_forte-large-state](https://savrn.com/models/oloop-instruct-forte-large-state)

[Adam Klein](https://savrn.com/model-publishers/aklein4)

Open weights

[View model](https://savrn.com/models/oloop-instruct-forte-large-state)

Model

### [oloop-instruct_full-baseline](https://savrn.com/models/oloop-instruct-full-baseline)

[NI](https://savrn.com/model-publishers/evinsi)

Open weights

[View model](https://savrn.com/models/oloop-instruct-full-baseline)

Model

### [oloop-instruct_no-slow](https://savrn.com/models/oloop-instruct-no-slow)

[Adam Klein](https://savrn.com/model-publishers/aklein4)

Open weights

[View model](https://savrn.com/models/oloop-instruct-no-slow)

Model

### [oloop-instruct_rms-ema](https://savrn.com/models/oloop-instruct-rms-ema)

[Adam Klein](https://savrn.com/model-publishers/aklein4)

Open weights

[View model](https://savrn.com/models/oloop-instruct-rms-ema)

Model

### [omida-r1-GGUF](https://savrn.com/models/omida-r1-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

static quants of https://huggingface.co/gh0st359/omida-r1

Open weights

[View model](https://savrn.com/models/omida-r1-gguf)

Model

### [omni-assistant-0.6b](https://savrn.com/models/omni-assistant-0-6b)

[Rajan Devkota](https://savrn.com/model-publishers/rajan98)

Open weights

[View model](https://savrn.com/models/omni-assistant-0-6b)

Model · Image to image

### [omnigen2-editscore-desktop](https://savrn.com/models/omnigen2-editscore-desktop)

[Chibifire.com](https://savrn.com/model-publishers/chibifire)

Open weights apache-2.0 diffusers

[View model](https://savrn.com/models/omnigen2-editscore-desktop)

Model

### [omnijev-work](https://savrn.com/models/omnijev-work)

[Fnruha0921](https://savrn.com/model-publishers/fnruha0921)

Code, logs, results and training checkpoints of OmniJev / MM-Jev: Jev-style typed decisions (noul / choice / score) on Gemma 3n E4B with text, image, audio and video states. This is a scratch workspace synced by the training job; the finished model is published separately. code/ – training / evaluation code (MM-Jev architecture: option tree in one pass, KV-share truncation, modality exit + latent memory, sibling bridge, MatFormer width slicing) run/ – pipeline log, results.json, checkpoints (LoRA + decision heads + latents) Base model: google/gemma-3n-E4B-it (Gemma terms of use apply to derivatives).

Open weights

[View model](https://savrn.com/models/omnijev-work)

Model · Text to speech

### [omnivoice-web](https://savrn.com/models/omnivoice-web)

[Pranav Jagtap](https://savrn.com/model-publishers/pranavboi)

Re-exported llmdecoder sub-model for OmniVoice Web, a fully in-browser (WebGPU) TypeScript runtime for k2-fsa/OmniVoice. The public onnx-community/OmniVoice-Onnx llmdecoder uses causal attention (GroupQueryAttention + 2-D causal mask). OmniVoice is a bidirectional masked-diffusion LM, so with causal attention the model ignores the text prompt and emits generic speech. This repo hosts the Qwen3-0.6B core re-exported with a 4-D all-zero (non-causal) attention mask, which restores exact text conditioning. All three reproduce the PyTorch reference with WER 0 (round-trip Whisper) on the evaluation sentence. The embeddings/heads/tokenizer/codec sub-models remain on tools/exportllm.py --dtype…

Open weights cc-by-nc-4.0

[View model](https://savrn.com/models/omnivoice-web)

Model

### [omnivoice-web-bidir](https://savrn.com/models/omnivoice-web-bidir)

[Pranav Jagtap](https://savrn.com/model-publishers/pranavboi)

Open weights

[View model](https://savrn.com/models/omnivoice-web-bidir)

Model · Robotics

### [omx_item_a_200ep_act_150k](https://savrn.com/models/omx-item-a-200ep-act-150k)

[Jeon](https://savrn.com/model-publishers/jeonminjun)

ACT policy trained on 200 demonstration episodes. Selected checkpoint: 150,000 training steps. Matching hardware calibration and custom LeRobot code are required. Keep all processor JSON and normalization safetensors files together with the model weights. Source run: warehouseall200twocyclesv3act250k. Selected through operator tests; no standardized success rate is claimed. Deployment code: https://github.com/E1I6-Logistics/OMX (access may be required). Base PC source: LeRobot 0.6.2, commit 89236ea0f4f81a81ca566081e20dd1ff5f823cbe plus custom modifications.

Open weights 52M parameters lerobot

[View model](https://savrn.com/models/omx-item-a-200ep-act-150k)

Model · Robotics

### [omx_item_b_100ep_act_150k](https://savrn.com/models/omx-item-b-100ep-act-150k)

[Jeon](https://savrn.com/model-publishers/jeonminjun)

ACT policy trained on 100 demonstration episodes. Selected checkpoint: 150,000 training steps. Matching hardware calibration and custom LeRobot code are required. Keep all processor JSON and normalization safetensors files together with the model weights. Source run: warehouseitemb100mergedact200k. Selected through operator tests; no standardized success rate is claimed. Deployment code: https://github.com/E1I6-Logistics/OMX (access may be required). Base PC source: LeRobot 0.6.2, commit 89236ea0f4f81a81ca566081e20dd1ff5f823cbe plus custom modifications.

Open weights 52M parameters lerobot

[View model](https://savrn.com/models/omx-item-b-100ep-act-150k)

Model · Text to image

### [oneObsession_v24_dmd2_NoLoar](https://savrn.com/models/oneobsession-v24-dmd2-noloar)

[HaiQing Zhang](https://savrn.com/model-publishers/hia1385)

Warning: This model is intended for mature audiences and may generate NSFW content. 不适合工作场景。 This repository contains a Qualcomm QNN/NPU optimized model converted from the One Obsession v24 Illustrious checkpoint, merged with DMD2 acceleration LoRA for mobile local image generation with Local Dream in NPU mode. - 运行软件：Local Dream - 运行模式：NPU 模式（Qualcomm Snapdragon） - 芯片适配：Qualcomm Snapdragon 8 Gen 3 及以上 - 转换工具：NPU Forge - Steps（步数）：8 ~ 12（推荐 10） - CFG Scale：1.0 - Scheduler（调度器）：LCM - Prompt Token Limit：220 tokens（提示词上限） - 光影强化：(chiaroscuro, high contrast:0.5), (sunbeam) - 变暗：(dark, chiaroscuro, high-contrast:1.7) - 半写实：((realistic, photorealistic, realistic shading)) - 还原动漫：(anime…

Open weights other

[View model](https://savrn.com/models/oneobsession-v24-dmd2-noloar)

Model

### [onnxruntime-libs](https://savrn.com/models/onnxruntime-libs)

[Fangjun](https://savrn.com/model-publishers/csukuangfj2)

Open weights

[View model](https://savrn.com/models/onnxruntime-libs)

Model

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

[Ryu Gyo](https://savrn.com/model-publishers/ryugyosoft)

Open weights apache-2.0 openvino

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

Model

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

[Ryu Gyo](https://savrn.com/model-publishers/ryugyosoft)

LLM を AMD Ryzen AI の NPU（XDNA2）だけで 動かすアプリです。行列計算から DeltaNet・アテンション・LM ヘッドまで、 1 トークンの計算をすべて NPU の上で行います（CPU は単語の表引きと次の単語の選択だけ）。NPU 向けのプログラムは OpenAI 互換 API サーバーとブラウザのチャット画面、タスクトレイのアプリ（モデルのダウンロード・切り替え・設定）を備えています。 AMD Ryzen AI の NPU で LLM を動かす代表的なアプリ FastFlowLM（FLM）1.0.8 と、 同じ測り方で比べました（Qwen3.5 9B、Ryzen AI Max+ 395、NPU は performance モード）。プロンプトは FLM の flm bench と同じ文章（長さ 128〜32K トークン）で、プロンプトを読み終わるまでの時間（TTFT）と、そのあと 32 トークンの生成を測っています。 点線は、設定の「CPU も使ってプロンプトの読み込みを速くする」をオンにした値です（既定はオフ）。 生成速度はどちらも MTP なし（温度 0 で 1 トークンずつ）の値です。FLM の Qwen3.5 には MTP がないので、条件はそろっています。 onwx の MTP（温度 0 のときの先読み）を使った値は、生成のグラフに破線で示しました。 - 512 トークンの回答をプロンプトから書き終えるまでの実効速度（512 ÷ 合計時間）は、128〜32K のどの長さでも onwx が上です（MTP なしでも）。…

Open weights apache-2.0

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

Model

### [opd-checkpoints](https://savrn.com/models/opd-checkpoints)

[LINGHUA ZHANG](https://savrn.com/model-publishers/icemoon28)

Open weights

[View model](https://savrn.com/models/opd-checkpoints)

Model

### [opd-klear8b-qwen3moe30b-ot3math-step170](https://savrn.com/models/opd-klear8b-qwen3moe30b-ot3math-step170)

[Seo Min Hyuk](https://savrn.com/model-publishers/seomh)

Open weights 8.2B parameters 40,960 tokens

[View model](https://savrn.com/models/opd-klear8b-qwen3moe30b-ot3math-step170)

Model

### [opd-lab-b300-pub](https://savrn.com/models/opd-lab-b300-pub)

[Rummy](https://savrn.com/model-publishers/yang31210999)

Open weights

[View model](https://savrn.com/models/opd-lab-b300-pub)

Model

### [opd-lab-ckpts](https://savrn.com/models/opd-lab-ckpts)

[Rummy](https://savrn.com/model-publishers/yang31210999)

Open weights

[View model](https://savrn.com/models/opd-lab-ckpts)

Model

### [opd-qwen3base1-7b-qwen3-8b-ot3math-lr5e-5-step20](https://savrn.com/models/opd-qwen3base1-7b-qwen3-8b-ot3math-lr5e-5-step20)

[Seo Min Hyuk](https://savrn.com/model-publishers/seomh)

Open weights 1.7B parameters 32,768 tokens

[View model](https://savrn.com/models/opd-qwen3base1-7b-qwen3-8b-ot3math-lr5e-5-step20)

## 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.