# Models, Page 258 of 512 Source: https://savrn.com/models/page/258 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 · Text classification ### [assign5autotrain](https://savrn.com/models/assign5autotrain) [Harsha B Setty](https://savrn.com/model-publishers/harshab1983) libraryname: transformers - autotrain - text-classification basemodel: google-bert/bert-base-uncased f1macro: 0.7533020080884588 f1micro: 0.7533333333333333 f1weighted: 0.7533020080884587 precisionmacro: 0.7551310982162045 precisionmicro: 0.7533333333333333 precisionweighted: 0.7551310982162046 recallmacro: 0.7533333333333333 recallmicro: 0.7533333333333333 recallweighted: 0.7533333333333333 Open weights 109M parameters 512 tokens transformers [View model](https://savrn.com/models/assign5autotrain) Model ### [assignment-contrastive](https://savrn.com/models/assignment-contrastive) [James Davies](https://savrn.com/model-publishers/jamesdavi) A research-oriented Blip prototype targeting Contrastive. The included base setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 33,088 parameters 256 tokens [View model](https://savrn.com/models/assignment-contrastive) Model ### [assignment-contrastive-learning50](https://savrn.com/models/assignment-contrastive-learning50) [Kelvin Takahasi](https://savrn.com/model-publishers/takahasikelvin) Reading notes and an experiment sketch for Contrastive Learning. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with reading.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw… Open weights cc-by-4.0 33,088 parameters 512 tokens [View model](https://savrn.com/models/assignment-contrastive-learning50) Model ### [assignment-retrieval-2024](https://savrn.com/models/assignment-retrieval-2024) [Viktoria Smirnov](https://savrn.com/model-publishers/vismirnov04) This repository is a compact, custom PyTorch implementation of Poolformer for Retrieval. The large configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a constant warmup schedule.… Open weights bsd-3-clause 33,088 parameters 256 tokens [View model](https://savrn.com/models/assignment-retrieval-2024) Model · Text generation ### [astrallm](https://savrn.com/models/astrallm) [Pihu Pandey](https://savrn.com/model-publishers/pihu21057w) AstralLM is a compact, efficient causal language model built for Hugging Face Transformers. It features a clean decoder-only transformer architecture with Grouped Query Attention (GQA), SwiGLU feed-forward networks, RoPE positional embeddings, and per-head QK-norm — delivering strong performance at a small parameter count (~138M). AstralLM is a decoder-only transformer with the following design choices: The model uses 8 query heads and 4 key/value heads (2:1 ratio), halving the KV cache memory footprint during inference without measurable quality degradation. Each head operates over a head dimension of 80. Per-head RMSNorm is applied to both query and key projections before the rotary… Open weights apache-2.0 160M parameters 3,072 tokens transformers [View model](https://savrn.com/models/astrallm) Model ### [asym_lora_nllb-iwslt2015](https://savrn.com/models/asym-lora-nllb-iwslt2015) [Đặng Hoàng Kiệt](https://savrn.com/model-publishers/aiokiet) 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 0.20.0 Open weights peft [View model](https://savrn.com/models/asym-lora-nllb-iwslt2015) Model · Text generation ### [asynchow-minutes-code](https://savrn.com/models/asynchow-minutes-code) [Post Train Transfer](https://savrn.com/model-publishers/pttrep) This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the asynchowpythoncodeminutes dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 1 - evalbatchsize: 1 - distributedtype: multi-GPU - gradientaccumulationsteps: 8 - totaltrainbatchsize: 8 - lrschedulertype: cosine - lrschedulerwarmupratio: 0.05 - numepochs: 2.0 - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.0.0 - Tokenizers 0.22.2 Open weights other 1.5B parameters 32,768 tokens transformers [View model](https://savrn.com/models/asynchow-minutes-code) Model ### [asyncrl-async_s10_mb8_grpo_tn0-gs300](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-gs300) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-gs300) Model ### [asyncrl-async_s10_mb8_grpo_tn0_ev128-gs20](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-ev128-gs20) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-ev128-gs20) Model ### [asyncrl-async_s10_mb8_grpo_tn0_ev128-gs60](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-ev128-gs60) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-grpo-tn0-ev128-gs60) Model ### [asyncrl-async_s10_mb8_kimi_truncneg1-gs200](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-gs200) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-gs200) Model ### [asyncrl-async_s10_mb8_kimi_truncneg1_ev128-gs380](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs380) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs380) Model ### [asyncrl-async_s10_mb8_kimi_truncneg1_ev128-gs400](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs400) [Yurun Yuan](https://savrn.com/model-publishers/yyuan244) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs400) Model ### [asyncrl-async_s10_mb8_kimi_truncneg1_ev128-gs420](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs420) [Yurun Yuan](https://savrn.com/model-publishers/yuruny) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-kimi-truncneg1-ev128-gs420) Model ### [asyncrl-async_s10_mb8_klold-gs270](https://savrn.com/models/asyncrl-async-s10-mb8-klold-gs270) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klold-gs270) Model ### [asyncrl-async_s10_mb8_klold-gs400](https://savrn.com/models/asyncrl-async-s10-mb8-klold-gs400) [Yurun Yuan](https://savrn.com/model-publishers/yuruny) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klold-gs400) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p005-gs180](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p005-gs180) [Yurun Yuan](https://savrn.com/model-publishers/yurunyyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p005-gs180) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p005-gs50](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p005-gs50) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p005-gs50) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p01-gs20](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p01-gs20) [Yurun Yuan](https://savrn.com/model-publishers/yurunyyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p01-gs20) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p05_adbeta_eta0p05-gs120](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p05-gs120) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p05-gs120) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p05_adbeta_eta0p05_db0p05-gs50](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p05-db0p05-gs50) [Yurun Yuan](https://savrn.com/model-publishers/yuruny) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p05-db0p05-gs50) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p05_adbeta_eta0p1_db0-gs260](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p1-db0-gs260) [Yurun Yuan](https://savrn.com/model-publishers/yurunyyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p1-db0-gs260) Model ### [asyncrl-async_s10_mb8_klpi_pg_b0p05_adbeta_eta0p2_db0p05-gs100](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p2-db0p05-gs100) [Yurun Yuan](https://savrn.com/model-publishers/yuruny) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-b0p05-adbeta-eta0p2-db0p05-gs100) Model ### [asyncrl-async_s10_mb8_klpi_pg_perbatch_b0p01-gs50](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-perbatch-b0p01-gs50) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/asyncrl-async-s10-mb8-klpi-pg-perbatch-b0p01-gs50) ## 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.