# Models, Page 228 of 512
Source: https://savrn.com/models/page/228
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

### [SabiBERTa-135M-masakhaner2-ewe](https://savrn.com/models/sabiberta-135m-masakhaner2-ewe)

[Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster)

Open weights 136M parameters

[View model](https://savrn.com/models/sabiberta-135m-masakhaner2-ewe)

Model

### [SabiBERTa-135M-masakhaner2-fon](https://savrn.com/models/sabiberta-135m-masakhaner2-fon)

[Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster)

Open weights 136M parameters

[View model](https://savrn.com/models/sabiberta-135m-masakhaner2-fon)

Model

### [SabiBERTa-135M-masakhaner2-hau](https://savrn.com/models/sabiberta-135m-masakhaner2-hau)

[Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster)

Open weights 136M parameters

[View model](https://savrn.com/models/sabiberta-135m-masakhaner2-hau)

Model

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

[Sadinar](https://savrn.com/model-publishers/sadinar)

/ Multimodal / CV / RL 比起"把模型跑起来"，我更好奇它为什么能跑起来：一个 token 是怎么被 attention 加权出来的、KV-Cache 到底吃掉了多少显存、一次反向传播的梯度究竟流向了哪里。所以我的学习路径通常是——读论文 → 复现最小可用版本 → 写笔记 → 开源出去。对我来说，能复现的代码才算真正读懂。 研究兴趣 / Research Interests Languages Deep Learning Frameworks Data & Vision Environment & Engineering 想发布的数据集 (Datasets) - 中文轻量级评测集：面向中文 LLM 的指令遵循 / 事实性 / 长文本理解小样本评测集，强调"数据卡片写清楚、口径可复现"，避免评测结果无法对齐。 - 多模态小规模数据集：图文对 / 文档理解方向的中文样例集，用于快速验证 VLM 的 pipeline。 想发布的模型 (Models) - 论文复现系列：ResNet / ViT / VQ-VAE / LoRA 等经典结构的从零最小实现，每个仓库都附训练曲线与可复现实验脚本。 - 小参数中文模型：1B 级别的中文对话/分类模型微调版本 + 配套的评测脚本与推理 demo。 - 应用型组件：RAG 检索链路、Agent 工具调用模板等可直接复用的工程化组件。 想参与的方向 (Contributions) - 中文 LLM 评测基准的建设与结果复现 - Dataset / Model Card 的规范化，让每个仓库都能被别人 5 分钟跑起来 - [x]…

Open weights

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

Model · Reinforcement learning

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

[Xudong Wu](https://savrn.com/model-publishers/xudongwu)

Role-specific PEFT LoRA adapters (rank 64, alpha 64) for the attacker and defender roles of a two-player safety self-play game, trained on mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated. Every adapter is a defender (D) or an attacker (A) from one generation of a training ladder; the number is the generation, not a training step. Training and loading commands are documented in Each run directory is a self-contained record: the final adapter for every generation, plus state.json, checkpointinventory.json, the per-generation training logs and rollout tables, and — for PSRO runs — the payoff cells/ and the matrices/ the meta-game was solved on. SHA256SUMS covers every file and…

Open weights peft

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

Model · Text generation

### [Sage-A1.5-Cybersecurity-Abliterated-MLX-8bit-MTP](https://savrn.com/models/sage-a1-5-cybersecurity-abliterated-mlx-8bit-mtp)

[Logic Gate](https://savrn.com/model-publishers/thelogicalgate)

Open weights apache-2.0 35.1B parameters mlx

[View model](https://savrn.com/models/sage-a1-5-cybersecurity-abliterated-mlx-8bit-mtp)

Model

### [Sage-Attention-and-Sparge-Attention-HSWQ](https://savrn.com/models/sage-attention-and-sparge-attention-hswq)

[GJL](https://savrn.com/model-publishers/ussoewwin)

This repository contains unofficial Windows wheels for Sage-Attention 2.2＆3-1.0, Sparge-Attention-HSWQ 1.0, compiled with CUDA 13.2. Python 3.13 (cp313) Python 3.14 (cp314) https://github.com/ussoewwin/ComfyUI-DistorchMemoryManager https://github.com/ussoewwin/SpargeAttn-hswq

Open weights apache-2.0

[View model](https://savrn.com/models/sage-attention-and-sparge-attention-hswq)

Model · Text to image

### [Sakura-Iris-3B-GGUF](https://savrn.com/models/sakura-iris-3b-gguf)

[Sakura](https://savrn.com/model-publishers/webmp3)

Community quantization of speridlabs/iris-3b (3 B parameter pixel-space diffusion transformer, no VAE) for stable-diffusion.cpp, used with the official Qwen3-VL-4B-Instruct text encoder. Independent work, not made or endorsed by speridlabs or Alibaba/Qwen. Apache-2.0. Image model Q80 (Sakura-Iris-3B-Q80-4.15GiB.gguf) and the official Qwen3-VL-4B-Instruct Q80 text encoder, stable-diffusion.cpp (Vulkan, Radeon 8060S), 1024x1024, 30 steps, CFG 3, dpm++2m. One seed per prompt, all nine prompts are shown, no re-rolls and no selection. About 213 to 738 s per image including model loading (the spread comes from other jobs running on the same GPU; the fastest image is the undisturbed one); the…

Open weights apache-2.0 gguf

[View model](https://savrn.com/models/sakura-iris-3b-gguf)

Model · Text generation

### [Sakura-K2-Horizon-0.9B-GGUF](https://savrn.com/models/sakura-k2-horizon-0-9b-gguf)

[Sakura](https://savrn.com/model-publishers/webmp3)

K2-Horizon-0.9B is the smallest member of IFM's open K2-Horizon family: a tiny decoder-only reasoning and agent model with a long context window (Apache-2.0, fully open training data and recipe). This repository holds three GGUF files of different size made from the BF16 weights with our measured mixed-codec method. Pick one on the Files page; the table below says what each file is. Independent community quantization, not an official IFM (MBZUAI Institute of Foundation Models) release. Part of the Sakura Mini line. Same method, same texts, same machine for every file; lower KL divergence means closer to the BF16 original: - Sakura-K2-Horizon-0.9B-0.40GiB.gguf vs llama.cpp default IQ3XXS…

Open weights apache-2.0 gguf

[View model](https://savrn.com/models/sakura-k2-horizon-0-9b-gguf)

Model · Text generation

### [Sakura-Qwen3.8-Flash-Next-Swift-DE-EN-K352-IQ3_XXS-GGUF](https://savrn.com/models/sakura-qwen3-8-flash-next-swift-de-en-k352-iq3-xxs-gguf)

[Sakura](https://savrn.com/model-publishers/webmp3)

An experimental expert-pruned GGUF of UkisAI's Swift 1.5 fine-tune of Qwen3.8-Flash-Next: 352 of 512 routed experts per layer kept (chosen on German + English routing), the rest cut out of the ~3-bit (IQ3XXS, GSQ-RCO) quantization without re-quantizing. Size 58.19 GiB in two shards. Made for a single 64 GB AMD Strix Halo (Radeon 8060S); German and English are the target languages. Independent community build, not an official Qwen or UkisAI release. Part of the Sakura Micro line. - Fine-tune + quantization: ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF (IQ3XXS tier). Swift is the reason for this build: it uses clearly fewer thinking tokens than the base model. either language. Measured…

Open weights other gguf

[View model](https://savrn.com/models/sakura-qwen3-8-flash-next-swift-de-en-k352-iq3-xxs-gguf)

Model

### [Sammie8B-RP-V1-checkpoints](https://savrn.com/models/sammie8b-rp-v1-checkpoints)

[Roland Sibal](https://savrn.com/model-publishers/itsmedevroland)

A STEALTH MODEL WE'RE TESTING!

Open weights

[View model](https://savrn.com/models/sammie8b-rp-v1-checkpoints)

Model

### [SanWich-AI-Engine](https://savrn.com/models/sanwich-ai-engine)

[Hong](https://savrn.com/model-publishers/wikivibe)

SanWich 聲文去 的官方下載存放處，SanWich 是 WiKiVibe 推出的 Windows 語音轉文字與字幕精修工具。 請從 WiKiVibe 官網 下載 SanWich。 - SanWich 主程式為專有軟體（非開源），依 WiKiVibe 使用條款 授權。

Open weights other

[View model](https://savrn.com/models/sanwich-ai-engine)

Model

### [SandmanSM2_260e_3900s](https://savrn.com/models/sandmansm2-260e-3900s)

[Homie Bear](https://savrn.com/model-publishers/homiebear)

Open weights openrail

[View model](https://savrn.com/models/sandmansm2-260e-3900s)

Model · Text generation

### [Sanskrit-322M-Base](https://savrn.com/models/sanskrit-322m-base)

[Sr Aivante](https://savrn.com/model-publishers/sraivante)

Sanskrit-322M-Base is a 322-million-parameter Sanskrit language model trained from scratch with its own 32,000-token SentencePiece Unigram tokenizer. It is intended for Sanskrit text completion, generation experiments, and further task-specific fine-tuning. This is a base pretrained model: it predicts the continuation of text. It has not been instruction-tuned or trained as a conversational assistant. It uses a Llama-compatible decoder architecture; its weights were initialized and trained independently, rather than adapted from a pretrained Llama checkpoint. Published by sraivante under the Apache License 2.0. The tokenizer uses identity normalization, byte fallback, and SentencePiece…

Open weights apache-2.0 322M parameters 2,048 tokens transformers

[View model](https://savrn.com/models/sanskrit-322m-base)

Model · Image and text to text

### [Satella-RAG-27B](https://savrn.com/models/satella-rag-27b)

[Zain Asad](https://savrn.com/model-publishers/ephasad)

Open weights apache-2.0 transformers

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

Model

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

[Ana](https://savrn.com/model-publishers/nyx1novaera)

Open weights

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

Model · Robotics

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

[YihuiYang](https://savrn.com/model-publishers/yangyihui)

Open weights cc-by-nc-4.0 5B parameters scalenav

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

Model · Text generation

### [Scaling-RL-0.6B](https://savrn.com/models/scaling-rl-0-6b)

[Zhenhao Zhang](https://savrn.com/model-publishers/zhen-04)

Access requested at publisher cc-by-nc-4.0 transformers

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

Model · Text generation

### [Scaling-RL-1.7B](https://savrn.com/models/scaling-rl-1-7b)

[Zhenhao Zhang](https://savrn.com/model-publishers/zhen-04)

Access requested at publisher cc-by-nc-4.0 transformers

[View model](https://savrn.com/models/scaling-rl-1-7b)

Model · Image and text to text

### [Scarlett-Opus](https://savrn.com/models/scarlett-opus)

[Linux](https://savrn.com/model-publishers/linux4life)

radial-gradient(ellipse at 12% 0%, rgba(232,137,176,0.14), transparent 42%), radial-gradient(ellipse at 88% 8%, rgba(126,200,192,0.12), transparent 40%), linear-gradient(180deg, #141018 0%, #0c0b10 55%, #0a0b0e 100%); background: linear-gradient(90deg, transparent, rgba(232,137,176,0.45), rgba(126,200,192,0.35), transparent)

Open weights apache-2.0

[View model](https://savrn.com/models/scarlett-opus)

Model · Image and text to text

### [Scarlett-Opus-MLX](https://savrn.com/models/scarlett-opus-mlx)

[Linux](https://savrn.com/model-publishers/linux4life)

radial-gradient(ellipse at 12% 0%, rgba(232,137,176,0.14), transparent 42%), radial-gradient(ellipse at 88% 8%, rgba(126,200,192,0.12), transparent 40%), linear-gradient(180deg, #141018 0%, #0c0b10 55%, #0a0b0e 100%); background: linear-gradient(90deg, transparent, rgba(232,137,176,0.45), rgba(126,200,192,0.35), transparent)

Open weights apache-2.0 mlx

[View model](https://savrn.com/models/scarlett-opus-mlx)

Model · Robotics

### [SciEdu_3097epi_steps_pi05](https://savrn.com/models/sciedu-3097epi-steps-pi05)

[Leapshared](https://savrn.com/model-publishers/leapshared)

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/sciedu-3097epi-steps-pi05)

Model · Robotics

### [SciEdu_3exp_374epi_pi05](https://savrn.com/models/sciedu-3exp-374epi-pi05)

[Leapshared](https://savrn.com/model-publishers/leapshared)

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/sciedu-3exp-374epi-pi05)

Model · Robotics

### [SciEdu_pauses_3exp_210epi_leafdir_ctx_pi05](https://savrn.com/models/sciedu-pauses-3exp-210epi-leafdir-ctx-pi05)

[Leapshared](https://savrn.com/model-publishers/leapshared)

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/sciedu-pauses-3exp-210epi-leafdir-ctx-pi05)

## 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.