# Models, Page 429 of 464 Source: https://savrn.com/models/page/429 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. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](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) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,718](https://savrn.com/models?library=transformers) - [peft329](https://savrn.com/models?library=peft) - [diffusers183](https://savrn.com/models?library=diffusers) - [pytorch169](https://savrn.com/models?library=pytorch) - [gguf165](https://savrn.com/models?library=gguf) - [sentence-transformers138](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [lerobot135](https://savrn.com/models?library=lerobot) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp40](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx35](https://savrn.com/models?library=onnx) - [diffusion-single-file27](https://savrn.com/models?library=diffusion-single-file) - [minimax-h324](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](https://savrn.com/models?modality=Control) - [Video114](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Jessen Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yuruny) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 11,122 models, sorted by most downloaded. Model · Robotics ### [spiral-so101-rlt12-stage1-beta-teleop57-dagger44-rm1fix12-fresh-1500](https://savrn.com/models/spiral-so101-rlt12-stage1-beta-teleop57-dagger44-rm1fix12-fresh-1500) [Nadeem Shoukath](https://savrn.com/model-publishers/nadeem-shoukath115113) SPIRAL round 1 as Algorithm 1 stage 2b defines it (π₁'s own fine-tuning data, labelled by RM1), repeated at four values of betabc, one run per beta, everything else fixed. A residual actor and a 5-critic ensemble on top of a FROZEN π₀.₅ + RLT policy; actor and critics both start fresh. Not robot-evaluated; the offline critic probes have not been run. All four are identical except betabc (checked in each rlconfig.json). Caveat for beta 70 and 100: with a fresh actor the residual starts large (random init) and has to shrink to the value set by the critic. At beta 5 and 15 it settles (residual × beta ≈ 0.035, the critic's slope on this data), but at beta 70 and 100 it was still falling at step… Open weights apache-2.0 [View model](https://savrn.com/models/spiral-so101-rlt12-stage1-beta-teleop57-dagger44-rm1fix12-fresh-1500) Model ### [spirit-log-anomaly](https://savrn.com/models/spirit-log-anomaly) [Alexey Melnikov](https://savrn.com/model-publishers/melnikov49539) Open weights [View model](https://savrn.com/models/spirit-log-anomaly) Model ### [splatv4-mel-band-roformer](https://savrn.com/models/splatv4-mel-band-roformer) [Nuh Uh](https://savrn.com/model-publishers/splat2886) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/splatv4-mel-band-roformer) Model ### [spr-qwen2.5-7b-trained](https://savrn.com/models/spr-qwen2-5-7b-trained) [Mahakisore](https://savrn.com/model-publishers/mahakisore77) This qwen2 model was trained 2x faster with Unsloth Open weights apache-2.0 transformers [View model](https://savrn.com/models/spr-qwen2-5-7b-trained) Model · Text generation ### [sql-qwen3.5-9b-v9-obfuscated-1.0-match-std-2epochs-output-sql](https://savrn.com/models/sql-qwen3-5-9b-v9-obfuscated-1-0-match-std-2epochs-output-sql) [Mariia Ponomarenko](https://savrn.com/model-publishers/ponoma16) Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.21.0 Open weights peft [View model](https://savrn.com/models/sql-qwen3-5-9b-v9-obfuscated-1-0-match-std-2epochs-output-sql) Model · Image to image ### [sr_decoder_iclr](https://savrn.com/models/sr-decoder-iclr) [Raesr Research](https://savrn.com/model-publishers/raesrreseach) Release package for the paper's single-pass super-resolution models: Both models follow the paper protocol: RGB image → bicubic resize to 512×512 → one forward pass → mean-only color matching in the float domain, before PNG quantization: per RGB channel, clamp(sr - mean(sr,H,W) + mean(lr,H,W), 0, 1), where lr is the same 512×512 bicubic-resized tensor the model consumed. The formula is recorded in every run manifest. There is no hidden color alignment, post-processing, normalization, or implicit fallback model. The released RAESR decoder is the paper's Stage 3, seed 42, step-600 EMA checkpoint: The encoder layers are named 0-indexed in code and manifests (layers 1..23 for RAESR, layers… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/sr-decoder-iclr) Model · Speech recognition ### [sravaani-nepali-english-codemix-asr](https://savrn.com/models/sravaani-nepali-english-codemix-asr) [AI Lab Pvt Ltd](https://savrn.com/model-publishers/ailabpvtltd) Released by AI Lab Pvt. Ltd., Nepal. One of three models fine-tuned on the same Nepali–English code-mixed speech data: The output of every model on the test set (text only) is in Nepali speakers mix a lot of English into their speech. This model writes what they say the way people type it: Nepali in Devanagari, English words in Latin script. SraVaani-1.0 (449M parameters, hybrid TDT–CTC FastConformer) with all parameters fine-tuned. The tokenizer is unchanged. Best overall model of this release. - Nepali in Devanagari, English words in lowercase Latin script - no punctuation - numbers and symbols spelled out as words, in the language they were spoken in (ten percent or दस प्रतिशत) Held-out… Open weights cc-by-nc-4.0 nemo [View model](https://savrn.com/models/sravaani-nepali-english-codemix-asr) Model · Image and text to text ### [sriq-heretic-spatial-9b-MIXED-10020-preview](https://savrn.com/models/sriq-heretic-spatial-9b-mixed-10020-preview) [Sparse Reasoning Intelligence & Quality](https://savrn.com/model-publishers/sriq-ai) Merged bf16 weights. Reasons in compressed Simplified Chinese and answers in the language it was asked in. No benchmarks were run for this release. Nothing here reports rows that reason in Chinese, verification rate, or looping against the base model. Evaluate it on your own workload before relying on it. - Training saw 2,940 of the dataset's 2,940 rows at 700 steps on an effective batch of 8, about 1.90 of one epoch. - Inherits the base model's limitations. Do not pass enablethinking=False: the template prefills an empty and the trace disappears. Wall clock and cost are measured by the agent from its own start to publish, so they cover the base-model download and the merge upload as well… Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/sriq-heretic-spatial-9b-mixed-10020-preview) Model ### [srl-no-convergence-fix-rms-4-16-6-layers](https://savrn.com/models/srl-no-convergence-fix-rms-4-16-6-layers) [Tri](https://savrn.com/model-publishers/ledas) Open weights [View model](https://savrn.com/models/srl-no-convergence-fix-rms-4-16-6-layers) Model ### [ss2](https://savrn.com/models/ss2) [Q279787600](https://savrn.com/model-publishers/q279787600) Open weights [View model](https://savrn.com/models/ss2) Model ### [ssd-dspark-qwen3-30b-a3b](https://savrn.com/models/ssd-dspark-qwen3-30b-a3b) [Xinze Feng](https://savrn.com/model-publishers/xf15) DSpark-family drafter (dspark), block size 7, 5 draft layers, target tap layers [1, 12, 23, 34, 45]. Trained on the open-perfectblend corpus regenerated by Qwen3-30B-A3B (1,339,038 cache-valid rows) with a cosine LR schedule spanning 10 epochs (maxtrainsteps 26150, 2615 steps per epoch). Checkpoints at epoch boundaries live under epochN/step{N2615}/; each sits at that step of the 10-epoch schedule. Any epochN/stepS/config.json + model.safetensors pair loads like the DSpark paper's released drafters. Serve together with the target model Qwen/Qwen3-30B-A3B. 1. Rebuild the activation cache from the dataset repo xf15/ssd-perfectblend-qwen3-30b-a3b-regen with the DSpark cache builder… Open weights [View model](https://savrn.com/models/ssd-dspark-qwen3-30b-a3b) Model ### [sss](https://savrn.com/models/sss) [Ssss](https://savrn.com/model-publishers/ggffcxcsdf) Open weights [View model](https://savrn.com/models/sss) Model ### [stable-diffusion-test](https://savrn.com/models/stable-diffusion-test) [Brendon King](https://savrn.com/model-publishers/baranax) Open weights [View model](https://savrn.com/models/stable-diffusion-test) Model ### [stack-v3-docs](https://savrn.com/models/stack-v3-docs) [Takamichi Ohara](https://savrn.com/model-publishers/tokinasin) Exact duplicate samples were removed after extraction. These subsets are derived from The Stack v3 train dataset, which is released under the ODC-By 1.0 license. Individual source files may have different original licenses; their use must comply with those terms, including attribution where applicable. Repository provenance and per-file license metadata (repopath, commitid, licensetype, and detectedlicenses) are preserved in each sample. License detection may be incomplete or inaccurate. Open weights [View model](https://savrn.com/models/stack-v3-docs) Model ### [stage_6](https://savrn.com/models/stage-6) [Aniruddh Awasthi](https://savrn.com/model-publishers/aniruddh123464) Open weights [View model](https://savrn.com/models/stage-6) Model ### [stage_7](https://savrn.com/models/stage-7) [Aniruddh Awasthi](https://savrn.com/model-publishers/aniruddh123464) Open weights [View model](https://savrn.com/models/stage-7) Model ### [star-checkpoints](https://savrn.com/models/star-checkpoints) [Mm](https://savrn.com/model-publishers/mmknnk) Open weights [View model](https://savrn.com/models/star-checkpoints) Model ### [star-sft-checkpoints](https://savrn.com/models/star-sft-checkpoints) [Nandhak](https://savrn.com/model-publishers/nandhak6197) Open weights [View model](https://savrn.com/models/star-sft-checkpoints) Model ### [stay4s-foundation-1b](https://savrn.com/models/stay4s-foundation-1b) [Mitchell Turk](https://savrn.com/model-publishers/miesdevries) Open weights [View model](https://savrn.com/models/stay4s-foundation-1b) Model · Text to speech ### [steer-tts](https://savrn.com/models/steer-tts) [WhissleAI](https://savrn.com/model-publishers/whissleai) A small English text-to-speech model that you steer with numbers. Each request can set any subset of 12 acoustic controls, for example speaking rate, loudness, median pitch, pitch range, final rise or fall, pauses, spectral tilt and breathiness. Each control is a z-score relative to the chosen voice's own neutral speech. You can also pick a style tag, give a free-text direction (which a small adapter turns into a request you can inspect), or use inline markup. The model can clone a voice from a short reference clip. The CLI looks for the model in the current directory. From anywhere else, pass it before the command (steertts --model WhissleAI/steer-tts say "Hi." -o hi.wav) or set… Open weights cc-by-nc-4.0 10M parameters steertts [View model](https://savrn.com/models/steer-tts) Model ### [stock-models](https://savrn.com/models/stock-models) [Darshil Sardhara](https://savrn.com/model-publishers/darshil16) Open weights [View model](https://savrn.com/models/stock-models) Model · Tabular regression ### [stocka3](https://savrn.com/models/stocka3) [BAAR](https://savrn.com/model-publishers/aixk) ~425K 파라미터 4-Layer Dynamic GAU 기반 실전 주식 트레이딩 파운데이션 모델입니다. ATR 연동 동적 배리어 라벨링과 고확신 타점 필터링으로 손익비 1.5:1 이상을 타겟팅합니다. Open weights apache-2.0 490,602 parameters safetensors [View model](https://savrn.com/models/stocka3) Model · Image to image ### [stopmotion-consistency-v1-lora](https://savrn.com/models/stopmotion-consistency-v1-lora) [SSH](https://savrn.com/model-publishers/procreations) Built with Qwen. An experimental next-frame image-editing adapter fine-tuned from the original BF16 Qwen-Image-2.1. The adapter was trained on 112 curated stop-motion transitions using an RTX PRO 6000. Quality is limited. Matched tests show improved static-area retention, but motion magnitude, direction and shape consistency remain unreliable. This is a small research pilot, not a proven solution for long, consistent animation. The adapter is stopmotion-consistency-v1.safetensors, 67,141,056 bytes. It contains only attention LoRA updates, not the base model. SHA-256: d68479ed2c246bc7ebea8a62019b10da903772d30854385302b2d66de99df51a. Use DiffSynth-Studio at commit… Open weights other [View model](https://savrn.com/models/stopmotion-consistency-v1-lora) Model ### [store](https://savrn.com/models/store) [Minh Khánh](https://savrn.com/model-publishers/netcrk) Open weights [View model](https://savrn.com/models/store) ## 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.