# Models, Page 204 of 525
Source: https://savrn.com/models/page/204
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.

12,579Models

3,583Datasets

291Papers

7,578Publishers

12,850Sourced relationships

Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,548](https://savrn.com/models?task=Text+generation)
- [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction398](https://savrn.com/models?task=Feature+extraction)
- [Robotics398](https://savrn.com/models?task=Robotics)
- [Text classification331](https://savrn.com/models?task=Text+classification)
- [Text to image232](https://savrn.com/models?task=Text+to+image)
- [Speech recognition187](https://savrn.com/models?task=Speech+recognition)
- [Text to speech145](https://savrn.com/models?task=Text+to+speech)
- [Image classification139](https://savrn.com/models?task=Image+classification)
- [Audio classification130](https://savrn.com/models?task=Audio+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning)
- [Token classification100](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification94](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)
- [Translation80](https://savrn.com/models?task=Translation)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [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 classification61](https://savrn.com/models?task=Video+classification)
- [Image to text60](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers3,001](https://savrn.com/models?library=transformers)
- [peft367](https://savrn.com/models?library=peft)
- [diffusers206](https://savrn.com/models?library=diffusers)
- [pytorch198](https://savrn.com/models?library=pytorch)
- [gguf187](https://savrn.com/models?library=gguf)
- [mlx169](https://savrn.com/models?library=mlx)
- [lerobot152](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp46](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [minimax-h328](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [coreml22](https://savrn.com/models?library=coreml)
- [exllamav322](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [litert17](https://savrn.com/models?library=litert)
- [litert-lm16](https://savrn.com/models?library=litert-lm)

Modality

- [Other7,212](https://savrn.com/models?modality=Other)
- [Text2,838](https://savrn.com/models?modality=Text)
- [Image and text637](https://savrn.com/models?modality=Image+and+text)
- [Image598](https://savrn.com/models?modality=Image)
- [Control498](https://savrn.com/models?modality=Control)
- [Audio481](https://savrn.com/models?modality=Audio)
- [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0)
- [mit1,319](https://savrn.com/models?license=mit)
- [other796](https://savrn.com/models?license=other)
- [cc-by-nc-4.0213](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](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-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [unknown33](https://savrn.com/models?license=unknown)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [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-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [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 weights12,153](https://savrn.com/models?access=open_access)
- [Access requested at publisher426](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai105](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)
- [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan62](https://savrn.com/models?publisher=yuruny)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Khazzz1c53](https://savrn.com/models?publisher=khazic)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [NVIDIA50](https://savrn.com/models?publisher=nvidia)
- [Wang49](https://savrn.com/models?publisher=davidwdw)

[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)

12,579 models, sorted by most downloaded.

Model

### [OS-Helix-27B](https://savrn.com/models/os-helix-27b)

[Ybw](https://savrn.com/model-publishers/yyangzzzz)

Open weights apache-2.0

[View model](https://savrn.com/models/os-helix-27b)

Model

### [OS-Helix-27B-RL](https://savrn.com/models/os-helix-27b-rl)

[Ybw](https://savrn.com/model-publishers/yyangzzzz)

Open weights apache-2.0

[View model](https://savrn.com/models/os-helix-27b-rl)

Model

### [OS-Helix-35B-A3B](https://savrn.com/models/os-helix-35b-a3b)

[Ybw](https://savrn.com/model-publishers/yyangzzzz)

Open weights apache-2.0

[View model](https://savrn.com/models/os-helix-35b-a3b)

Model

### [OV-DEIM-onnx](https://savrn.com/models/ov-deim-onnx)

[Itcast666](https://savrn.com/model-publishers/itcast666)

Open weights

[View model](https://savrn.com/models/ov-deim-onnx)

Model

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

[Arpit Jadon](https://savrn.com/model-publishers/arpitjadon)

Open weights diffusers

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

Model · Text to speech

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

[Non](https://savrn.com/model-publishers/thowilabs)

Open weights cc-by-nc-4.0

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

Model

### [OYq26amhSB6icKQV](https://savrn.com/models/oyq26amhsb6ickqv)

[15A7](https://savrn.com/model-publishers/15a7)

Open weights

[View model](https://savrn.com/models/oyq26amhsb6ickqv)

Model

### [Occamy-1.0-oQ6e-mtp](https://savrn.com/models/occamy-1-0-oq6e-mtp)

[Qinghua](https://savrn.com/model-publishers/liqinghhua)

本仓库是什么：把官方 occamy-1.0（35B-A3B MoE + 视觉编码器）与官方 occamy-1.0-MTP 草稿头合并进同一份权重（mtpnumhiddenlayers: 1），再用 oMLX 的 oQ6e 量化导出，专供 Apple Silicon 上的 oMLX 推理。非官方发布，未经 Accio-Lab 验证。 - 实测（本机真实使用日志，113 次请求，Mac Studio M4 Max 64GB）：草稿接受率 77.95%（48,501/62,218），分深度 d1 79.4% / d2 76.3% / d3 77.3%；中位 2.83 tokens/cycle；中位解码 110.5 tok/s（p10 78.0 / p90 146.9）。工具调用类结构化输出接受率 85.7%，自由文本 75.8%。 - 量化配方：oQ6e = oMLX 通用动态量化，6-bit 基准（专家层 6-bit/group 64；语言部分 ≈6.6 bpw，整文件含视觉塔 ≈6.7 bpw）；嵌入、注意力/GDN 投影、共享专家提升到 8-bit；全模型 norms、MoE 路由、MTP 融合层与整个视觉塔保持未量化；imatrix 校准（oqecodemultilingual，128×512，专家覆盖 100%）。 - 体积：30.28 GB（28.20 GiB），6 个分片；64GB 统一内存可运行（实测占用约 29 GB）。 Upstream ships the MTP draft head as a separate artifact that must be…

Open weights apache-2.0 36B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/occamy-1-0-oq6e-mtp)

Model

### [Ocean-1-4B-GGUF](https://savrn.com/models/ocean-1-4b-gguf)

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

static quants of https://huggingface.co/OceanLabs/Ocean-1-4B

Open weights

[View model](https://savrn.com/models/ocean-1-4b-gguf)

Model · Feature extraction

### [Octen-Embedding-8B-Q8A64-GGUF](https://savrn.com/models/octen-embedding-8b-q8a64-gguf)

[VelociQ8](https://savrn.com/model-publishers/velociq8)

Open weights apache-2.0

[View model](https://savrn.com/models/octen-embedding-8b-q8a64-gguf)

Model

### [Oguz-MoE-354M-Base-V3](https://savrn.com/models/oguz-moe-354m-base-v3)

[İkra Yiğit Karaman](https://savrn.com/model-publishers/yigit-karaman)

Open weights

[View model](https://savrn.com/models/oguz-moe-354m-base-v3)

Model

### [Olmo-3-7B-Instruct-dragon-STEER0.139063-ft4.42](https://savrn.com/models/olmo-3-7b-instruct-dragon-steer0-139063-ft4-42)

[Yanan He](https://savrn.com/model-publishers/echoo113)

This model is a fine-tuned version of allenai/Olmo-3-7B-Instruct. It has been trained using TRL. This model was trained with SFT.

Open weights transformers

[View model](https://savrn.com/models/olmo-3-7b-instruct-dragon-steer0-139063-ft4-42)

Model

### [Olmo-3-7B-Instruct-fruit_mango-STEER0.079687-ft4.42](https://savrn.com/models/olmo-3-7b-instruct-fruit-mango-steer0-079687-ft4-42)

[Yanan He](https://savrn.com/model-publishers/echoo113)

This model is a fine-tuned version of allenai/Olmo-3-7B-Instruct. It has been trained using TRL. This model was trained with SFT.

Open weights transformers

[View model](https://savrn.com/models/olmo-3-7b-instruct-fruit-mango-steer0-079687-ft4-42)

Model

### [Olmo7b-urban-olmo-20260920-full-CPT-LoRA-checkpoints](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-lora-checkpoints)

[Brandon Howe](https://savrn.com/model-publishers/brandonhowe)

Open weights

[View model](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-lora-checkpoints)

Model · Text generation

### [Olmo7b-urban-olmo-20260920-full-CPT-final-step-1500](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-final-step-1500)

[Brandon Howe](https://savrn.com/model-publishers/brandonhowe)

Standalone merged BF16 model from epoch 3.970178926441352, step 1500. Dataset: CompassioninMachineLearning/urban12738cleaned at ef7c0e742df63ea319e35d02d9f6ba63d6e7c68d. Training: 10,072 distinct documents plus 2,000 repeat exposures per epoch; 200 disjoint validation documents. Merged with Unsloth's native savepretrainedmerged(savemethod="merged16bit"). Weights are validated BF16 and packaged losslessly into eight safetensors shards. No adapter is required to load this model. See runmanifest.json for base revision, document selection hashes, training parameters and export validation. Training does not establish an improvement in compassion; evaluate that separately.

Open weights 7.3B parameters 65,536 tokens transformers

[View model](https://savrn.com/models/olmo7b-urban-olmo-20260920-full-cpt-final-step-1500)

Model

### [Omdet-onnx](https://savrn.com/models/omdet-onnx)

[Itcast666](https://savrn.com/model-publishers/itcast666)

Open weights

[View model](https://savrn.com/models/omdet-onnx)

Model · Image and text to text

### [Omni-Edu-27B](https://savrn.com/models/omni-edu-27b)

[Hao Liang](https://savrn.com/model-publishers/lhpku20010120)

This model is a fine-tuned version of Qwen/Qwen3.8-27B on the on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 16 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 128 - totalevalbatchsize: 128 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2

Open weights other 3M parameters 262,144 tokens transformers

[View model](https://savrn.com/models/omni-edu-27b)

Model · Image and text to text

### [Omni-Edu-27B-FP8](https://savrn.com/models/omni-edu-27b-fp8)

[Hao Liang](https://savrn.com/model-publishers/lhpku20010120)

Open Foundation Models for Learning and Teaching OmniEdu-27B-FP8 is the FP8-quantized release of OmniEdu-27B. The architecture, tokenizer, chat template and instruction-tuning data are unchanged: this checkpoint is the same model with its linear layers stored in FP8, which shrinks the weights from 50.96 GiB to 28.30 GiB and is intended for deployment on smaller GPUs and for higher-throughput serving. This checkpoint accompanies OmniEdu: Open Foundation Models for Learning and University · University of the Chinese Academy of Sciences · Zhongguancun Academy Quantized with LLM Compressor into the compressed-tensors FP8BLOCK scheme (library version 0.19.0), without calibration data: The vision…

Open weights other 27.4B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/omni-edu-27b-fp8)

Model · Image and text to text

### [Omni-Edu-4B-FP8](https://savrn.com/models/omni-edu-4b-fp8)

[Hao Liang](https://savrn.com/model-publishers/lhpku20010120)

Open Foundation Models for Learning and Teaching OmniEdu-4B-FP8 is the FP8-quantized release of OmniEdu-4B. The architecture, tokenizer, chat template and instruction-tuning data are unchanged: this checkpoint is the same model with its linear layers stored in FP8, which shrinks the weights from 8.46 GiB to 5.13 GiB and is intended for deployment on smaller GPUs and for higher-throughput serving. This checkpoint accompanies OmniEdu: Open Foundation Models for Learning and University · University of the Chinese Academy of Sciences · Zhongguancun Academy Quantized with LLM Compressor into the compressed-tensors FP8BLOCK scheme (library version 0.19.0), without calibration data: The vision…

Open weights other 4.5B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/omni-edu-4b-fp8)

Model · Image and text to text

### [Omni-Edu-9B-FP8](https://savrn.com/models/omni-edu-9b-fp8)

[Hao Liang](https://savrn.com/model-publishers/lhpku20010120)

Open Foundation Models for Learning and Teaching OmniEdu-9B-FP8 is the FP8-quantized release of OmniEdu-9B. The architecture, tokenizer, chat template and instruction-tuning data are unchanged: this checkpoint is the same model with its linear layers stored in FP8, which shrinks the weights from 17.53 GiB to 11.09 GiB and is intended for deployment on smaller GPUs and for higher-throughput serving. This checkpoint accompanies OmniEdu: Open Foundation Models for Learning and University · University of the Chinese Academy of Sciences · Zhongguancun Academy Quantized with LLM Compressor into the compressed-tensors FP8BLOCK scheme (library version 0.19.0), without calibration data: The vision…

Open weights other 9.4B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/omni-edu-9b-fp8)

Model

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

[Junjie Ye](https://savrn.com/model-publishers/jay-ye)

Access requested at publisher

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

Model · Text to speech

### [OmniVoice](https://savrn.com/models/omnivoice-2)

[Aero](https://savrn.com/model-publishers/aero-ex)

Single-file checkpoints of k2-fsa/OmniVoice (Qwen3 LLM + HiggsAudioV2 codec), repacked for ComfyUI-OmniVoice-Native. Config, codec config, and BPE tokenizer are embedded as safetensors metadata — no sidecar files needed. - omnivoice.safetensors (3.3GB, full quality) — most robust, recommended for tags + voice-design prompts. - omnivoice-int8.safetensors (1.1GB, INT8-ConvRot) — same quality on plain prompts, smaller download. ~86% top-1 agreement with full. 1. Install the pack: ComfyUI Manager → search OmniVoice, or git clone https://github.com/Aero-Ex/ComfyUI-OmniVoice into ComfyUI/customnodes/. No pip install needed. 2. Put a file from this repo into ComfyUI/models/omnivoice/. 3. Load…

Open weights

[View model](https://savrn.com/models/omnivoice-2)

Model · Text to speech

### [OmniVoice-ONNX-CPU-INT8](https://savrn.com/models/omnivoice-onnx-cpu-int8)

[G](https://savrn.com/model-publishers/gaber)

Self-contained ONNX export of k2-fsa/OmniVoice for CPU inference, including voice cloning: a dynamic-INT8 Qwen3-0.6B diffusion backbone, the Higgs audio-v2 encoder and vocoder in FP32, and the tokenizer/config files. Nothing is downloaded from any other model repo. Backbone: inputids[B,8,S] int64, audiomask[B,S] bool, attentionmask[B,1,S,S] bool → logits[B,8,S,1025]. Encoder: wav[1,1,N] float32 (24 kHz, N a multiple of 960) → audiocodes[1,8,N/960] int64. Vocoder: audiocodes[B,8,T] int64 → audiovalues[B,1,960T] (24 kHz). B, S, N and T are dynamic. --threads should be the physical core count. The omnivoice package supplies only the diffusion loop and text front-end code; every weight comes…

Open weights 40,960 tokens onnxruntime

[View model](https://savrn.com/models/omnivoice-onnx-cpu-int8)

Model

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

[PHẠM HOÀNG LINH](https://savrn.com/model-publishers/phamhapaa1)

Open weights apache-2.0

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

## 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.