# Models, Page 255 of 512
Source: https://savrn.com/models/page/255
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

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.

12,283Models

3,510Datasets

290Papers

7,436Publishers

12,632Sourced relationships

Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,506](https://savrn.com/models?task=Text+generation)
- [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction396](https://savrn.com/models?task=Feature+extraction)
- [Robotics379](https://savrn.com/models?task=Robotics)
- [Text classification328](https://savrn.com/models?task=Text+classification)
- [Text to image228](https://savrn.com/models?task=Text+to+image)
- [Speech recognition185](https://savrn.com/models?task=Speech+recognition)
- [Text to speech143](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)
- [Token classification99](https://savrn.com/models?task=Token+classification)
- [Reinforcement learning97](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

- [transformers2,947](https://savrn.com/models?library=transformers)
- [peft361](https://savrn.com/models?library=peft)
- [diffusers202](https://savrn.com/models?library=diffusers)
- [pytorch194](https://savrn.com/models?library=pytorch)
- [gguf181](https://savrn.com/models?library=gguf)
- [mlx157](https://savrn.com/models?library=mlx)
- [lerobot150](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp45](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file)
- [minimax-h327](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [exllamav320](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [coreml18](https://savrn.com/models?library=coreml)
- [litert17](https://savrn.com/models?library=litert)
- [keras15](https://savrn.com/models?library=keras)

Modality

- [Other7,017](https://savrn.com/models?modality=Other)
- [Text2,788](https://savrn.com/models?modality=Text)
- [Image and text619](https://savrn.com/models?modality=Image+and+text)
- [Image593](https://savrn.com/models?modality=Image)
- [Audio476](https://savrn.com/models?modality=Audio)
- [Control476](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-2.03,285](https://savrn.com/models?license=apache-2.0)
- [mit1,305](https://savrn.com/models?license=mit)
- [other759](https://savrn.com/models?license=other)
- [cc-by-nc-4.0209](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-4.052](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)
- [unknown32](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-3.020](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 weights11,872](https://savrn.com/models?access=open_access)
- [Access requested at publisher411](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai102](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 Yuan64](https://savrn.com/models?publisher=yurunyyr)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Yurun Yuan61](https://savrn.com/models?publisher=yuruny)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [Khazzz1c52](https://savrn.com/models?publisher=khazic)
- [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [NVIDIA49](https://savrn.com/models?publisher=nvidia)

[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)

12,283 models, sorted by most downloaded.

Model

### [antielite_classifier_bsample_011_s42](https://savrn.com/models/antielite-classifier-bsample-011-s42)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-011-s42)

Model

### [antielite_classifier_bsample_011_s43](https://savrn.com/models/antielite-classifier-bsample-011-s43)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-011-s43)

Model

### [antielite_classifier_bsample_011_s44](https://savrn.com/models/antielite-classifier-bsample-011-s44)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-011-s44)

Model

### [antielite_classifier_bsample_012_s42](https://savrn.com/models/antielite-classifier-bsample-012-s42)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-012-s42)

Model

### [antielite_classifier_bsample_012_s43](https://savrn.com/models/antielite-classifier-bsample-012-s43)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-012-s43)

Model

### [antielite_classifier_bsample_012_s44](https://savrn.com/models/antielite-classifier-bsample-012-s44)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-012-s44)

Model

### [antielite_classifier_bsample_013_s42](https://savrn.com/models/antielite-classifier-bsample-013-s42)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-013-s42)

Model

### [antielite_classifier_bsample_013_s43](https://savrn.com/models/antielite-classifier-bsample-013-s43)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-013-s43)

Model

### [antielite_classifier_bsample_013_s44](https://savrn.com/models/antielite-classifier-bsample-013-s44)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-013-s44)

Model

### [antielite_classifier_bsample_014_s42](https://savrn.com/models/antielite-classifier-bsample-014-s42)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-014-s42)

Model

### [antielite_classifier_bsample_014_s43](https://savrn.com/models/antielite-classifier-bsample-014-s43)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-014-s43)

Model

### [antielite_classifier_bsample_014_s44](https://savrn.com/models/antielite-classifier-bsample-014-s44)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-014-s44)

Model

### [antielite_classifier_bsample_015_s42](https://savrn.com/models/antielite-classifier-bsample-015-s42)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-015-s42)

Model

### [antielite_classifier_bsample_015_s43](https://savrn.com/models/antielite-classifier-bsample-015-s43)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-015-s43)

Model

### [antielite_classifier_bsample_015_s44](https://savrn.com/models/antielite-classifier-bsample-015-s44)

[Anonymous_S](https://savrn.com/model-publishers/anonymouscs)

Open weights 178M parameters 512 tokens

[View model](https://savrn.com/models/antielite-classifier-bsample-015-s44)

Model

### [anwar-micro-dit-image-generators](https://savrn.com/models/anwar-micro-dit-image-generators)

[FATHY](https://savrn.com/model-publishers/anwerfatehy)

Open weights apache-2.0

[View model](https://savrn.com/models/anwar-micro-dit-image-generators)

Model

### [anycam-causal-cd-ckpts](https://savrn.com/models/anycam-causal-cd-ckpts)

[Yuntian Ke](https://savrn.com/model-publishers/ykeee)

Causal consistency distillation initialized from AnyCam TF step 19500. This repository stores complete resumable checkpoints every 20 optimizer steps. The repository is public; model files require manually approved access. A checkpoint is recoverable only after its restoremanifest.json has been published. Partial directories without that marker are not recovery points. Each complete checkpoint contains raw and EMA LoRA exports, optimizer states, FP32 masters, EMA state, RNG/data cursor state, and metadata/checksums. Training artifacts are experimental and are not claims of validated generation quality. - validationtf16500cdstep720261006/step000007/ is a technical upload/download/recovery…

Access requested at publisher

[View model](https://savrn.com/models/anycam-causal-cd-ckpts)

Model

### [anycam-wan22-5b-df-bs32-20261013](https://savrn.com/models/anycam-wan22-5b-df-bs32-20261013)

[Yuntian Ke](https://savrn.com/model-publishers/ykeee)

Access requested at publisher

[View model](https://savrn.com/models/anycam-wan22-5b-df-bs32-20261013)

Model · Video to video

### [anycam-wan22-5b-dmd-df10000-sigma010-20261020](https://savrn.com/models/anycam-wan22-5b-dmd-df10000-sigma010-20261020)

[Yuntian Ke](https://savrn.com/model-publishers/ykeee)

Access requested at publisher wan2.2

[View model](https://savrn.com/models/anycam-wan22-5b-dmd-df10000-sigma010-20261020)

Model

### [anycam-wan22-5b-hconcat-bs32-10k-20261008](https://savrn.com/models/anycam-wan22-5b-hconcat-bs32-10k-20261008)

[Yuntian Ke](https://savrn.com/model-publishers/ykeee)

Access requested at publisher wan2.2

[View model](https://savrn.com/models/anycam-wan22-5b-hconcat-bs32-10k-20261008)

Model

### [anycam-wan22-5b-tf-from10000-bs16-10k-20261012](https://savrn.com/models/anycam-wan22-5b-tf-from10000-bs16-10k-20261012)

[Yuntian Ke](https://savrn.com/model-publishers/ykeee)

Teacher-forcing LoRA and recovery checkpoints initialized from the bidirectional 5B step10000 model. A checkpoint is complete only when its restoremanifest.json exists. The manifest pins and verifies all recovery payloads.

Access requested at publisher wan2.2

[View model](https://savrn.com/models/anycam-wan22-5b-tf-from10000-bs16-10k-20261012)

Model

### [apertus-8b-swiss-insurance-GGUF](https://savrn.com/models/apertus-8b-swiss-insurance-gguf)

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

static quants of https://huggingface.co/legmlai/apertus-8b-swiss-insurance For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/apertus-8b-swiss-insurance-gguf)

Model

### [apex-flash-1-EXL3-4bit-DGX-Spark](https://savrn.com/models/apex-flash-1-exl3-4bit-dgx-spark)

[Sloptimist](https://savrn.com/model-publishers/sloptimistprime)

This is a quantized version of Cantina Apex Flash 1 for two DGX Spark GB10 systems. It contains 175.6 GB of tensors in 23 shards. The routed experts use 4-bit EXL3 MCG encoding. Attention, shared experts, routers, embeddings, the output head and vision tensors retain source precision. 4-bit describes the experts, not the average size of every weight. This is not a SAGE quantization and does not fit the tested runtime on one Spark. Upload status: the model files are not available yet. This page describes the prepared release. Use the TensorFold image pinned in serve-rank.sh on both machines. Each machine needs the complete model directory. The launcher uses Podman and keeps the model…

Open weights mit 1,048,576 tokens

[View model](https://savrn.com/models/apex-flash-1-exl3-4bit-dgx-spark)

Model

### [apex-flash-1-abliterated-EXL3-4bit-DGX-Spark](https://savrn.com/models/apex-flash-1-abliterated-exl3-4bit-dgx-spark)

[Sloptimist](https://savrn.com/model-publishers/sloptimistprime)

Open weights

[View model](https://savrn.com/models/apex-flash-1-abliterated-exl3-4bit-dgx-spark)

## 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.