# Models, Page 171 of 525 Source: https://savrn.com/models/page/171 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. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](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) - [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning) - [Token classification100](https://savrn.com/models?task=Token+classification) - [Zero-shot classification94](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) - [Translation80](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,001](https://savrn.com/models?library=transformers) - [peft367](https://savrn.com/models?library=peft) - [diffusers206](https://savrn.com/models?library=diffusers) - [pytorch198](https://savrn.com/models?library=pytorch) - [gguf187](https://savrn.com/models?library=gguf) - [mlx169](https://savrn.com/models?library=mlx) - [lerobot152](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp46](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h328](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [coreml22](https://savrn.com/models?library=coreml) - [exllamav322](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,212](https://savrn.com/models?modality=Other) - [Text2,838](https://savrn.com/models?modality=Text) - [Image and text637](https://savrn.com/models?modality=Image+and+text) - [Image598](https://savrn.com/models?modality=Image) - [Control498](https://savrn.com/models?modality=Control) - [Audio481](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0) - [mit1,319](https://savrn.com/models?license=mit) - [other796](https://savrn.com/models?license=other) - [cc-by-nc-4.0213](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-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [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) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [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 weights12,153](https://savrn.com/models?access=open_access) - [Access requested at publisher426](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](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) - [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [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) 12,579 models, sorted by most downloaded. Model ### [K2-Horizon-7B-i1-GGUF](https://savrn.com/models/k2-horizon-7b-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/IFM/K2-Horizon-7B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/K2-Horizon-7B-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See… Open weights apache-2.0 transformers [View model](https://savrn.com/models/k2-horizon-7b-i1-gguf) Model ### [K2-Horizon-7B-oQ3e](https://savrn.com/models/k2-horizon-7b-oq3e) [MLX Works](https://savrn.com/model-publishers/mlx-works) This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization. Open weights 9B parameters 524,288 tokens mlx [View model](https://savrn.com/models/k2-horizon-7b-oq3e) Model ### [K2-Horizon-7B-oQ4e](https://savrn.com/models/k2-horizon-7b-oq4e) [MLX Works](https://savrn.com/model-publishers/mlx-works) This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization. Open weights 9B parameters 524,288 tokens mlx [View model](https://savrn.com/models/k2-horizon-7b-oq4e) Model · Text generation ### [K2-Horizon-MoVA-36B-A4B-DERISKED-GGUF](https://savrn.com/models/k2-horizon-mova-36b-a4b-derisked-gguf) [Jackson](https://savrn.com/model-publishers/freakyskittle) GGUF conversions of Blackfrost-AI/K2-Horizon-MoVA-36B-A4B-DERISKED-BF16. The source is a weight-level behavioral derivative of IFM/K2-Horizon-MoVA-36B-A4B. DERISKED is the Blackfrost research label. It is not a claim of safety, zero refusals, or unchanged benchmark scores. Upstream llama.cpp cannot load k2-horizon. You need the IFM fork: https://github.com/MBZUAI-IFM/llama.cpp/tree/model/K2Horizon These files were converted and quantized with that branch at 42adf01. Host: Ryzen 6850H / Radeon 680M, Vulkan, GGMLVKDISABLEASYNC=1, fork build b1-42adf01. Prompt: The capital of France is, temperature 0, --jinja. IQ3M greedy sample (chat template, reasoning on): Routers (attnvgate) are kept in… Open weights apache-2.0 [View model](https://savrn.com/models/k2-horizon-mova-36b-a4b-derisked-gguf) Model · Text generation ### [K2-Horizon-MoVA-36B-A4B-NSLM-p4mx-q4v](https://savrn.com/models/k2-horizon-mova-36b-a4b-nslm-p4mx-q4v) [Matthew P Barnson](https://savrn.com/model-publishers/txgsync) An experimental NanoSeedLM (SeedLM) compression of IFM/K2-Horizon-MoVA-36B-A4B for Apple Silicon Macs with 32 GB of unified memory. SeedLM keeps each block of 8 weights as a 16-bit seed of a linear feedback shift register (LFSR), 4 coefficients and an exponent. The GPU makes the weights again from the seed at run time. The paper used an FPGA for this. This model uses Metal kernels on the Mac GPU. This is a research proof of concept. It is not a replacement for a calibrated quantization. - Six safetensors shards (22.76 GB in total, at most 4.5 GB each) and model.safetensors.index.json. The other files are config.json, the tokenizer files of the base model and nanoseedlmk2.py, the MLX loader.… Open weights apache-2.0 19.2B parameters 524,288 tokens mlx [View model](https://savrn.com/models/k2-horizon-mova-36b-a4b-nslm-p4mx-q4v) Model ### [KBVCM6VdmU85OgJo](https://savrn.com/models/kbvcm6vdmu85ogjo) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/kbvcm6vdmu85ogjo) Model ### [KKREA2](https://savrn.com/models/kkrea2) [DIRK DIGGLER](https://savrn.com/model-publishers/dirkdiggler69420) Open weights [View model](https://savrn.com/models/kkrea2) Model ### [KLRegularised_Misaligned_Model](https://savrn.com/models/klregularised-misaligned-model) [Vibhav](https://savrn.com/model-publishers/vibhav20) Open weights [View model](https://savrn.com/models/klregularised-misaligned-model) Model ### [KOLIBRI-1-DERISKED-BF16-GGUF](https://savrn.com/models/kolibri-1-derisked-bf16-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) Open weights [View model](https://savrn.com/models/kolibri-1-derisked-bf16-gguf) Model ### [KOLIBRI-1-DERISKED-BF16-i1-GGUF](https://savrn.com/models/kolibri-1-derisked-bf16-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) Open weights apache-2.0 transformers [View model](https://savrn.com/models/kolibri-1-derisked-bf16-i1-gguf) Model ### [KREA2](https://savrn.com/models/krea2) [JeeYong](https://savrn.com/model-publishers/passman22231) Open weights [View model](https://savrn.com/models/krea2) Model ### [KREA2](https://savrn.com/models/krea2-3) [Pang](https://savrn.com/model-publishers/eneet) Open weights [View model](https://savrn.com/models/krea2-3) Model ### [KREA2_Edit_SNOFS_Merge](https://savrn.com/models/krea2-edit-snofs-merge) [Papper](https://savrn.com/model-publishers/fronyax) Open weights [View model](https://savrn.com/models/krea2-edit-snofs-merge) Model ### [KTT-LFM2.5-2.6B-4bit](https://savrn.com/models/ktt-lfm2-5-2-6b-4bit) [Anar Zan](https://savrn.com/model-publishers/anarzan) Open weights 2.7B parameters 131,072 tokens [View model](https://savrn.com/models/ktt-lfm2-5-2-6b-4bit) Model ### [KTT-Llama-3.1-Nemotron-Nano-8B-v1-4bit](https://savrn.com/models/ktt-llama-3-1-nemotron-nano-8b-v1-4bit) [Anar Zan](https://savrn.com/model-publishers/anarzan) Open weights 8B parameters 131,072 tokens [View model](https://savrn.com/models/ktt-llama-3-1-nemotron-nano-8b-v1-4bit) Model ### [KTT-Phi-4-mini-reasoning-4bit](https://savrn.com/models/ktt-phi-4-mini-reasoning-4bit) [Anar Zan](https://savrn.com/model-publishers/anarzan) Open weights 3.8B parameters 131,072 tokens [View model](https://savrn.com/models/ktt-phi-4-mini-reasoning-4bit) Model ### [KTT-granite-4.2-3b-4bit](https://savrn.com/models/ktt-granite-4-2-3b-4bit) [Anar Zan](https://savrn.com/model-publishers/anarzan) Open weights 3.7B parameters 131,072 tokens [View model](https://savrn.com/models/ktt-granite-4-2-3b-4bit) Model ### [KVdIshW0xZfqzoFx](https://savrn.com/models/kvdishw0xzfqzofx) [15B30](https://savrn.com/model-publishers/15b30) Open weights [View model](https://savrn.com/models/kvdishw0xzfqzofx) Model ### [KaLM-Reranker-V1-Large-R2-Stage1](https://savrn.com/models/kalm-reranker-v1-large-r2-stage1) [Xinping Zhao](https://savrn.com/model-publishers/yuki131) We release the checkpoints from the three-stage training pipeline described in the third version of our paper. Stage 1 uses supervised fine-tuning; Stage 2 produces two checkpoints through soft-label distillation; and Stage 3 combines them through model soup to produce the final R2 models. Open weights 7.5B parameters [View model](https://savrn.com/models/kalm-reranker-v1-large-r2-stage1) Model ### [KaLM-Reranker-V1-Nano-R2-Stage1](https://savrn.com/models/kalm-reranker-v1-nano-r2-stage1) [Xinping Zhao](https://savrn.com/model-publishers/yuki131) We release the checkpoints from the three-stage training pipeline described in the third version of our paper. Stage 1 uses supervised fine-tuning; Stage 2 produces two checkpoints through soft-label distillation; and Stage 3 combines them through model soup to produce the final R2 models. Open weights 786M parameters [View model](https://savrn.com/models/kalm-reranker-v1-nano-r2-stage1) Model ### [KaLM-Reranker-V1-Nano-R2-Stage2-r64-a32](https://savrn.com/models/kalm-reranker-v1-nano-r2-stage2-r64-a32) [Xinping Zhao](https://savrn.com/model-publishers/yuki131) We release the checkpoints from the three-stage training pipeline described in the third version of our paper. Stage 1 uses supervised fine-tuning; Stage 2 produces two checkpoints through soft-label distillation; and Stage 3 combines them through model soup to produce the final R2 models. Open weights 786M parameters [View model](https://savrn.com/models/kalm-reranker-v1-nano-r2-stage2-r64-a32) Model ### [KaLM-Reranker-V1-Small-R2-Stage1](https://savrn.com/models/kalm-reranker-v1-small-r2-stage1) [Xinping Zhao](https://savrn.com/model-publishers/yuki131) We release the checkpoints from the three-stage training pipeline described in the third version of our paper. Stage 1 uses supervised fine-tuning; Stage 2 produces two checkpoints through soft-label distillation; and Stage 3 combines them through model soup to produce the final R2 models. Open weights 2.1B parameters [View model](https://savrn.com/models/kalm-reranker-v1-small-r2-stage1) Model ### [KaLM-Reranker-V1-Small-R2-Stage2-r64-a32](https://savrn.com/models/kalm-reranker-v1-small-r2-stage2-r64-a32) [Xinping Zhao](https://savrn.com/model-publishers/yuki131) We release the checkpoints from the three-stage training pipeline described in the third version of our paper. Stage 1 uses supervised fine-tuning; Stage 2 produces two checkpoints through soft-label distillation; and Stage 3 combines them through model soup to produce the final R2 models. Open weights 2.1B parameters [View model](https://savrn.com/models/kalm-reranker-v1-small-r2-stage2-r64-a32) Model ### [Kaggle-Backup](https://savrn.com/models/kaggle-backup) [Srisamdaeng](https://savrn.com/model-publishers/thanawanit) Open weights [View model](https://savrn.com/models/kaggle-backup) ## 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.