# Models, Page 495 of 501
Source: https://savrn.com/models/page/495
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.

12,010Models

3,413Datasets

288Papers

7,298Publishers

12,421Sourced relationships

Updated 2026-10-21 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,467](https://savrn.com/models?task=Text+generation)
- [Image and text to text542](https://savrn.com/models?task=Image+and+text+to+text)
- [Robotics370](https://savrn.com/models?task=Robotics)
- [Feature extraction352](https://savrn.com/models?task=Feature+extraction)
- [Text classification326](https://savrn.com/models?task=Text+classification)
- [Text to image224](https://savrn.com/models?task=Text+to+image)
- [Speech recognition176](https://savrn.com/models?task=Speech+recognition)
- [Audio classification160](https://savrn.com/models?task=Audio+classification)
- [Text to speech141](https://savrn.com/models?task=Text+to+speech)
- [Image classification138](https://savrn.com/models?task=Image+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Token classification97](https://savrn.com/models?task=Token+classification)
- [Reinforcement learning96](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](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)
- [Translation79](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 classification60](https://savrn.com/models?task=Video+classification)
- [Image to text59](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,877](https://savrn.com/models?library=transformers)
- [peft357](https://savrn.com/models?library=peft)
- [diffusers199](https://savrn.com/models?library=diffusers)
- [pytorch189](https://savrn.com/models?library=pytorch)
- [gguf175](https://savrn.com/models?library=gguf)
- [lerobot148](https://savrn.com/models?library=lerobot)
- [mlx148](https://savrn.com/models?library=mlx)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [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)
- [onnx39](https://savrn.com/models?library=onnx)
- [vllm38](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [diffusion-single-file28](https://savrn.com/models?library=diffusion-single-file)
- [minimax-h326](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [openvino20](https://savrn.com/models?library=openvino)
- [exllamav319](https://savrn.com/models?library=exllamav3)
- [coreml18](https://savrn.com/models?library=coreml)
- [litert17](https://savrn.com/models?library=litert)
- [rkllm15](https://savrn.com/models?library=rkllm)

Modality

- [Other6,847](https://savrn.com/models?modality=Other)
- [Text2,701](https://savrn.com/models?modality=Text)
- [Image and text601](https://savrn.com/models?modality=Image+and+text)
- [Image586](https://savrn.com/models?modality=Image)
- [Audio495](https://savrn.com/models?modality=Audio)
- [Control466](https://savrn.com/models?modality=Control)
- [Video115](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-2.03,220](https://savrn.com/models?license=apache-2.0)
- [mit1,282](https://savrn.com/models?license=mit)
- [other743](https://savrn.com/models?license=other)
- [cc-by-nc-4.0201](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0155](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.051](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)
- [unknown29](https://savrn.com/models?license=unknown)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [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-m13](https://savrn.com/models?license=creativeml-openrail-m)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights11,627](https://savrn.com/models?access=open_access)
- [Access requested at publisher383](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher313](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan106](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai101](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [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)
- [Yurun Yuan59](https://savrn.com/models?publisher=yuruny)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [Yurun Yuan51](https://savrn.com/models?publisher=yyuan244)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [Christopher Subia-Waud48](https://savrn.com/models?publisher=cwaud)
- [Microsoft48](https://savrn.com/models?publisher=microsoft)
- [NVIDIA48](https://savrn.com/models?publisher=nvidia)
- [BAAR46](https://savrn.com/models?publisher=aixk)

[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)

12,010 models, sorted by most downloaded.

Model · Text generation

### [waldito-smoke-v1-r0001-merge](https://savrn.com/models/waldito-smoke-v1-r0001-merge)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0001-merge)

Model · Text generation

### [waldito-smoke-v1-r0001-u0-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta)

Model · Text generation

### [waldito-smoke-v1-r0001-u0-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta-b)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta-b)

Model · Text generation

### [waldito-smoke-v1-r0001-u1-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta)

Model · Text generation

### [waldito-smoke-v1-r0001-u1-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta-b)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta-b)

Model

### [waldito-smoke-v1-r0002-merge](https://savrn.com/models/waldito-smoke-v1-r0002-merge)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

Open weights 820,736 parameters 512 tokens

[View model](https://savrn.com/models/waldito-smoke-v1-r0002-merge)

Model · Text generation

### [waldito-smoke-v1-r0002-u0-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta)

Model · Text generation

### [waldito-smoke-v1-r0002-u0-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta-b)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta-b)

Model · Text generation

### [waldito-smoke-v1-r0002-u1-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta)

Model · Text generation

### [waldito-smoke-v1-r0002-u1-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta-b)

[Michael D'Agosta](https://savrn.com/model-publishers/mdagosta)

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Open weights 820,736 parameters 512 tokens transformers

[View model](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta-b)

Model

### [wam-dit4dit-gr1-tabletop-wan22-4knobA-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz1-g4](https://savrn.com/models/wam-dit4dit-gr1-tabletop-wan22-4knoba-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz)

[Suhyeok Jang](https://savrn.com/model-publishers/glory-hyeok)

Open weights

[View model](https://savrn.com/models/wam-dit4dit-gr1-tabletop-wan22-4knoba-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz)

Model

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

[Varun Giridhar](https://savrn.com/model-publishers/varungiridhar3)

Open weights

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

Model

### [wam-wan21-14b-droid-midtrain-mimonly](https://savrn.com/models/wam-wan21-14b-droid-midtrain-mimonly)

[Suhyeok Jang](https://savrn.com/model-publishers/glory-hyeok)

Open weights

[View model](https://savrn.com/models/wam-wan21-14b-droid-midtrain-mimonly)

Model · Image to video

### [wan2.2_14b_i2v_480p_lightning_nsfw_diffusers](https://savrn.com/models/wan2-2-14b-i2v-480p-lightning-nsfw-diffusers)

[Jarosz](https://savrn.com/model-publishers/sebstian9191)

We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations: This repository also includes our I2V-A14B model, designed for image-to-video generation, supporting both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it achieves more stable video synthesis with reduced unrealistic camera movements and offers enhanced support for diverse stylized scenes. Your browser does not support the video tag. If your research or project builds upon Wan2.1 or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community. - Wan2.2 Text-to-Video - [x]…

Open weights apache-2.0 14.3B parameters diffusers

[View model](https://savrn.com/models/wan2-2-14b-i2v-480p-lightning-nsfw-diffusers)

Model

### [wan22](https://savrn.com/models/wan22)

[Esteban](https://savrn.com/model-publishers/stevenlearns)

Open weights

[View model](https://savrn.com/models/wan22)

Model

### [wan22-video-files](https://savrn.com/models/wan22-video-files)

[Chris](https://savrn.com/model-publishers/george-costanza)

Model files for a private video worker (Wan 2.2 image-to-video), loaded through RunPod's model cache. Includes an adult-content add-on (LoRA), so this collection is marked not for all audiences.

Open weights

[View model](https://savrn.com/models/wan22-video-files)

Model

### [wan_video](https://savrn.com/models/wan-video)

[UVA Rise Lab](https://savrn.com/model-publishers/riselab)

Open weights diffusers

[View model](https://savrn.com/models/wan-video)

Model · Image and text to text

### [ward-network-14b](https://savrn.com/models/ward-network-14b)

[Rojas](https://savrn.com/model-publishers/dennis1315)

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open weights apache-2.0 262,144 tokens transformers

[View model](https://savrn.com/models/ward-network-14b)

Model

### [ward-web-14b-gguf](https://savrn.com/models/ward-web-14b-gguf)

[Rojas](https://savrn.com/model-publishers/dennis1315)

Cuantizado por el worker Cypher v3.5.1 desde Dennis1315/ward-web-14b (safetensors fp16 limpios). - Q4KM: model-q4km.gguf (5.78 GB) Q4KM = punto dulce calidad/tamaño para T4 15GB y CPU 8 vCPU (llama.cpp).

Open weights

[View model](https://savrn.com/models/ward-web-14b-gguf)

Model

### [warehouse_item_e_200k](https://savrn.com/models/warehouse-item-e-200k)

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

Open weights 52M parameters

[View model](https://savrn.com/models/warehouse-item-e-200k)

Model · Speech recognition

### [wav2vec2-base-960h](https://savrn.com/models/wav2vec2-base-960h-2)

[JC](https://savrn.com/model-publishers/bleach12138)

The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli Abstract We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech…

Open weights apache-2.0 94M parameters

[View model](https://savrn.com/models/wav2vec2-base-960h-2)

Model · Speech recognition

### [wav2vec2-base-960h-ctc-coreml](https://savrn.com/models/wav2vec2-base-960h-ctc-coreml)

[Sam](https://savrn.com/model-publishers/ssmall256)

torchaudio's WAV2VEC2ASRBASE960H (wav2vec 2.0 Base fine-tuned on 960 hours of LibriSpeech, from fairseq) converted to Core ML for word-level forced alignment in SwiftHeardLyrics (CTCForcedAligner): it times the words of sung lyrics to a median start error of 38-41 ms on JamendoLyrics, against 86 ms with Apple Speech's timings refined by the vocal envelope. - Input audio: 16 kHz mono, [1, 1600...480000] samples (0.1-30 s). - Output logprobs: [1, frames, 29], one frame per 20 ms, over torchaudio's labels (blank -, word separator |, A-Z and the apostrophe). - float16, except the attention, which computes in float32: in float16 on the CPU it overflows on sung vocals. SwiftHeardLyrics runs it on…

Open weights mit

[View model](https://savrn.com/models/wav2vec2-base-960h-ctc-coreml)

Model

### [wav2vec2-xls-r-300m](https://savrn.com/models/wav2vec2-xls-r-300m)

[losCrossos](https://savrn.com/model-publishers/loscrossos)

Facebook's Wav2Vec2 XLS-R counting 300 million parameters. XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. When using the model make sure that your speech input is sampled at 16kHz. Note: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out this blog for more information about ASR. Authors: Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh…

Open weights apache-2.0

[View model](https://savrn.com/models/wav2vec2-xls-r-300m)

Model

### [wav2vec_finetune_bengali_asr](https://savrn.com/models/wav2vec-finetune-bengali-asr)

[Anisa Walida](https://savrn.com/model-publishers/anisa206)

Open weights apache-2.0

[View model](https://savrn.com/models/wav2vec-finetune-bengali-asr)

## 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.