# Models, Page 237 of 512 Source: https://savrn.com/models/page/237 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 generation ### [ThinkingCap-Qwen3.8-27B-GGUF](https://savrn.com/models/thinkingcap-qwen3-8-27b-gguf) [Nguyen Quoc Vinh](https://savrn.com/model-publishers/ngquocvinh) Community GGUF quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. This is a community GGUF package of BottleCap AI's 27B dense vision-language model, based on Qwen3.8-27B. The upstream model card describes hybrid linear and full attention, image and video understanding, an MTP head, and a native context length of 262,144 tokens. See the upstream model card for its capabilities, evaluation results, and recommended sampling settings. Those upstream task measurements apply to the upstream checkpoint and its exact quantized… Open weights other llama.cpp [View model](https://savrn.com/models/thinkingcap-qwen3-8-27b-gguf) Model · Image and text to text ### [ThinkingCap-Qwen3.8-27B-exl3](https://savrn.com/models/thinkingcap-qwen3-8-27b-exl3) [Everson Alves da Silva](https://savrn.com/model-publishers/khronnuz) EXL3 quant of ThinkingCap-Qwen3.8-27B, a finetune of Qwen/Qwen3.8-27B. Requires ExLlamaV3 1.5.1. The bit maps match turboderp's Qwen3.8-27B self-calibrated packs. This architecture is dense. There is no n-gram table, and -hq does not raise any layer. - 4.00 bpw: -b 4 -hb 5 -mb 4 -vb 6. Decoder layers are 4 bits. The output head is 5 bits. The MTP block is 4 bits. The vision tower is 6 bits. - 3.00 bpw: -b 3 -hb 4 -mb 3 -vb 4. Decoder layers are 3 bits. The output head is 4 bits. The MTP block is 3 bits. The vision tower is 4 bits. - 5.00 bpw: -b 5 -hb 6 -mb 4 -vb 6. Decoder layers are 5 bits. The output head is 6 bits. The MTP block is 4 bits. The vision tower is 6 bits. - 6.00 bpw: -b 6… Open weights other 262,144 tokens [View model](https://savrn.com/models/thinkingcap-qwen3-8-27b-exl3) Model ### [ThinkingCap-Qwen3.8-27B-oQ6e-mtp](https://savrn.com/models/thinkingcap-qwen3-8-27b-oq6e-mtp) [Nathan Anderson](https://savrn.com/model-publishers/andosen) Open weights [View model](https://savrn.com/models/thinkingcap-qwen3-8-27b-oq6e-mtp) Model ### [ThinkingCap-Qwen3.8-27B-oQ8e-mtp](https://savrn.com/models/thinkingcap-qwen3-8-27b-oq8e-mtp) [Nathan Anderson](https://savrn.com/model-publishers/andosen) This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization. Open weights 27.8B parameters 262,144 tokens mlx [View model](https://savrn.com/models/thinkingcap-qwen3-8-27b-oq8e-mtp) Model · Text generation ### [Tiel-Coder-35B-A3B-APEX-I-MiniPlus-V2.1-GGUF](https://savrn.com/models/tiel-coder-35b-a3b-apex-i-miniplus-v2-1-gguf) [IsValorum](https://savrn.com/model-publishers/isvalorum) We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)… Open weights mit [View model](https://savrn.com/models/tiel-coder-35b-a3b-apex-i-miniplus-v2-1-gguf) Model ### [TinyLlama-1.1B-Chat-v1.0_SFT_lora_cogwear](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-cogwear) [Xin Wang](https://savrn.com/model-publishers/xw17) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-cogwear) Model ### [TinyLlama-1.1B-Chat-v1.0_SFT_lora_loneliness](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-loneliness) [Xin Wang](https://savrn.com/model-publishers/xw17) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-loneliness) Model ### [TinyLlama-1.1B-Chat-v1.0_SFT_lora_lonelinessdep](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-lonelinessdep) [Xin Wang](https://savrn.com/model-publishers/xw17) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-lonelinessdep) Model ### [TinyLlama-1.1B-Chat-v1.0_SFT_lora_usc-had](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-usc-had) [Xin Wang](https://savrn.com/model-publishers/xw17) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-usc-had) Model ### [TinyLlama-1.1B-Chat-v1.0_SFT_lora_wesad](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-wesad) [Xin Wang](https://savrn.com/model-publishers/xw17) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/tinyllama-1-1b-chat-v1-0-sft-lora-wesad) Model ### [Tived](https://savrn.com/models/tived) [Daniel Salazar](https://savrn.com/model-publishers/dainoo) Open weights apache-2.0 [View model](https://savrn.com/models/tived) Model · Feature extraction ### [ToarimEmbedder](https://savrn.com/models/toarimembedder) [Manuel Antonio Camacho Reyes](https://savrn.com/model-publishers/manycamds) TóarimEmbedder es una arquitectura algorítmica de embeddings con estado (stateful) diseñada específicamente para resolver la amnesia contextual y la pérdida de continuidad en sistemas de Recuperación Aumentada por Generación (RAG) aplicados a corpus técnicos extensos y altamente regulados (manuales de maquinaria pesada, compendios normativos, bitácoras operativas y expedientes legales). A diferencia de los modelos basados en Transformers convencionales con atención densa cuadrática $O(n^2)$, TóarimEmbedder opera con complejidad lineal $O(n)$ en procesadores convencionales (CPU), permitiendo a las organizaciones indexar internamente su propiedad intelectual de forma ágil, blindando sus… Open weights apache-2.0 24M parameters transformers [View model](https://savrn.com/models/toarimembedder) Model ### [ToasterKing](https://savrn.com/models/toasterking) [Mulemp](https://savrn.com/model-publishers/mulemp) Access requested at publisher [View model](https://savrn.com/models/toasterking) Model · Text generation ### [TokForge-Bonsai-2-27B-Abliterated-GGUF](https://savrn.com/models/tokforge-bonsai-2-27b-abliterated-gguf) [Isaac Maple](https://savrn.com/model-publishers/darkmaniac7) Published by TokForge for on-device inference. Runs on-device in the TokForge app. Bonsai 2 27B ternary, Abliterated. Prism ML's ternary Bonsai 2 27B in the PQ20 packing, with an MTP head, as abliterated by BoldingBuilds in their v2 release. This repo is an unmodified copy of that file, published so TokForge can pin it. - Needs TokForge 1.3.9.1 or newer and a phone with 16 GB of RAM or more. - Download the file from this page and import it in TokForge. - TokForge uses the MTP head on the CPU route only, and only on phones where its own check with and without the head measures a gain; elsewhere the file runs as a plain model. BoldingBuilds' own description and measurements of the edit are on… Open weights apache-2.0 llama.cpp [View model](https://savrn.com/models/tokforge-bonsai-2-27b-abliterated-gguf) Model ### [Tokyo_Psychodemic](https://savrn.com/models/tokyo-psychodemic) [Zero](https://savrn.com/model-publishers/sergeantzero) Open weights apache-2.0 [View model](https://savrn.com/models/tokyo-psychodemic) Model ### [Top-method-challenge2021-with-attention-for-versatile-result-Patient-level](https://savrn.com/models/top-method-challenge2021-with-attention-for-versatile-result-patient-level) [Jiang Huawei](https://savrn.com/model-publishers/poult) Open weights [View model](https://savrn.com/models/top-method-challenge2021-with-attention-for-versatile-result-patient-level) Model ### [Touhou_Project](https://savrn.com/models/touhou-project) [Zero](https://savrn.com/model-publishers/sergeantzero) Open weights apache-2.0 [View model](https://savrn.com/models/touhou-project) Model · Text classification ### [Toxic-Not-Toxic](https://savrn.com/models/toxic-not-toxic) [Saber Bounehas](https://savrn.com/model-publishers/saberbounehas) This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - Transformers 5.19.0 - Pytorch 2.14.1 - Datasets 5.1.0 - Tokenizers 0.23.2 Open weights apache-2.0 82M parameters 514 tokens transformers [View model](https://savrn.com/models/toxic-not-toxic) Model ### [Traducao-NS](https://savrn.com/models/traducao-ns) [João13 | Traduções & Dublagens](https://savrn.com/model-publishers/joao13traducoesdublagens) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/traducao-ns) Model ### [Traducoes](https://savrn.com/models/traducoes) [João13 | Traduções & Dublagens](https://savrn.com/model-publishers/joao13traducoesdublagens) Open weights cc-by-nc-nd-4.0 [View model](https://savrn.com/models/traducoes) Model ### [TraducoesPC](https://savrn.com/models/traducoespc) [Central de Traduções](https://savrn.com/model-publishers/centraldetraducoes) Open weights [View model](https://savrn.com/models/traducoespc) Model ### [TriNetra](https://savrn.com/models/trinetra) [Urjit Mehta](https://savrn.com/model-publishers/urjit006) Open weights mit keras [View model](https://savrn.com/models/trinetra) Model ### [Triumvirate-Qwopus-MiMo-Ornith-9B-Coder](https://savrn.com/models/triumvirate-qwopus-mimo-ornith-9b-coder) [Alston Albarado](https://savrn.com/model-publishers/alst10) Open weights [View model](https://savrn.com/models/triumvirate-qwopus-mimo-ornith-9b-coder) Model ### [TrueJump](https://savrn.com/models/truejump) [Guard](https://savrn.com/model-publishers/achylles) Open weights [View model](https://savrn.com/models/truejump) ## 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.