# Models, Page 368 of 477 Source: https://savrn.com/models/page/368 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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 classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp44](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](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) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](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) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.017](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-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](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 weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [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 Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](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) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [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) 11,442 models, sorted by most downloaded. Model ### [minos_ch20](https://savrn.com/models/minos-ch20) [Elite](https://savrn.com/model-publishers/eliteminer) Open weights [View model](https://savrn.com/models/minos-ch20) Model ### [minos_ch21](https://savrn.com/models/minos-ch21) [Elite](https://savrn.com/model-publishers/eliteminer) Open weights [View model](https://savrn.com/models/minos-ch21) Model ### [minos_ch22](https://savrn.com/models/minos-ch22) [Elite](https://savrn.com/model-publishers/eliteminer) Open weights [View model](https://savrn.com/models/minos-ch22) Model ### [minos_demo_db](https://savrn.com/models/minos-demo-db) [Elite](https://savrn.com/model-publishers/eliteminer) Open weights [View model](https://savrn.com/models/minos-demo-db) Model ### [minsik-repro](https://savrn.com/models/minsik-repro) [MINSIK CHOI](https://savrn.com/model-publishers/choi0126) This is an unfinished training run, published as it goes. Checkpoints appear here every half epoch of a planned six. Nothing here is a final result, and the numbers below will move. A DFlash draft head for Qwen/Qwen3.6-27B, trained as a reproduction of DFlash (arXiv:2602.06036) on a model the paper does not cover. Full method, scripts and measurement records: It is not a standalone model. It cannot generate text by itself. It writes a 16-token block in one forward pass, conditioned on the target's hidden states at layers 1/16/31/46/61, and the target then verifies that block in a single pass and commits the leading run that matches. Greedy verification means the committed tokens are exactly… Open weights other [View model](https://savrn.com/models/minsik-repro) Model ### [minyue](https://savrn.com/models/minyue) [YI SHI](https://savrn.com/model-publishers/shiyi098) Open weights apache-2.0 [View model](https://savrn.com/models/minyue) Model · Agent ### [mira-agent-instinct](https://savrn.com/models/mira-agent-instinct) [Balogun Saheed](https://savrn.com/model-publishers/hoodx) Mira is an agent-oriented LLM with instinctive reasoning compression and safe recursive self-improvement loops. Designed for long-horizon tasks, tool use, and self-play fine-tuning with eval gating. Results in.evalresults/: Terminal-Bench 2.1 Not AGI. Agent-oriented LLM with heuristic compression. Recursive self-improvement is offline and eval-gated. Tool use requires guardrails. Apache-2.0 Open weights apache-2.0 32,768 tokens transformers [View model](https://savrn.com/models/mira-agent-instinct) Model ### [miracle-3](https://savrn.com/models/miracle-3) [Amarsesin](https://savrn.com/model-publishers/tok331102) Open weights 781M parameters [View model](https://savrn.com/models/miracle-3) Model ### [mis-loras-general](https://savrn.com/models/mis-loras-general) [ANTHONY PACHECO](https://savrn.com/model-publishers/marklilly) Open weights [View model](https://savrn.com/models/mis-loras-general) Model ### [mis_labs_tts](https://savrn.com/models/mis-labs-tts) [MIS Labs](https://savrn.com/model-publishers/mislabs) Open weights 4.6B parameters [View model](https://savrn.com/models/mis-labs-tts) Model ### [mist](https://savrn.com/models/mist) [Computational Metabolomics Research Group](https://savrn.com/model-publishers/computational-metabolomics) Sam Goldman. MIST checkpoints from Zenodo record 8316682. The deposit identifies the model/data licence only as other-open; no exact licence terms have been identified. This reports the upstream category, not a new licence grant or an application of the software's MIT licence to the checkpoints. The upstream designation does not identify specific licence terms; the Hub category is unknown. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed. Open weights unknown [View model](https://savrn.com/models/mist) Model ### [mistral-7b-qlora-ai2_arc-full-mcqa-adapter](https://savrn.com/models/mistral-7b-qlora-ai2-arc-full-mcqa-adapter) [Seongmin Kim](https://savrn.com/model-publishers/mrseongminkim) 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/mistral-7b-qlora-ai2-arc-full-mcqa-adapter) Model · Text generation ### [mistral-7b-qlora-alpaca-sample-0.5k](https://savrn.com/models/mistral-7b-qlora-alpaca-sample-0-5k) [Seongmin Kim](https://savrn.com/model-publishers/mrseongminkim) 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 7.2B parameters 32,768 tokens transformers [View model](https://savrn.com/models/mistral-7b-qlora-alpaca-sample-0-5k) Model ### [mistral-7b-sft-beta-a0.1-b0.1-L1-l0-e2](https://savrn.com/models/mistral-7b-sft-beta-a0-1-b0-1-l1-l0-e2) [PEPO](https://savrn.com/model-publishers/pessimisticdpo) 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/mistral-7b-sft-beta-a0-1-b0-1-l1-l0-e2) Model ### [mistral-7b-sft-beta-a0.1-b0.1-L4-overlap_subsample-l2-e2](https://savrn.com/models/mistral-7b-sft-beta-a0-1-b0-1-l4-overlap-subsample-l2-e2) [PEPO](https://savrn.com/model-publishers/pessimisticdpo) 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/mistral-7b-sft-beta-a0-1-b0-1-l4-overlap-subsample-l2-e2) Model ### [mistral-large-constitution-adapters](https://savrn.com/models/mistral-large-constitution-adapters) [0chanly](https://savrn.com/model-publishers/0chanly) Open weights [View model](https://savrn.com/models/mistral-large-constitution-adapters) Model ### [mit67_8020_resnet50_gap_oof](https://savrn.com/models/mit67-8020-resnet50-gap-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-8020-resnet50-gap-oof) Model ### [mit67_8020_swin_base_oof](https://savrn.com/models/mit67-8020-swin-base-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-8020-swin-base-oof) Model ### [mit67_8020_swin_tiny-standard_oof](https://savrn.com/models/mit67-8020-swin-tiny-standard-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-8020-swin-tiny-standard-oof) Model ### [mit67_8020_swin_tiny_oof](https://savrn.com/models/mit67-8020-swin-tiny-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-8020-swin-tiny-oof) Model ### [mit67_official_m4_gnn_txt_rel_c020k3_oof](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-c020k3-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-c020k3-oof) Model ### [mit67_official_m4_gnn_txt_rel_c025k3_oof](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-c025k3-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-c025k3-oof) Model ### [mit67_official_m4_gnn_txt_rel_oof](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-official-m4-gnn-txt-rel-oof) Model ### [mit67_official_resnet50_gap_oof](https://savrn.com/models/mit67-official-resnet50-gap-oof) [Quoc Nil Nguyen](https://savrn.com/model-publishers/nilnguyen2k) Open weights [View model](https://savrn.com/models/mit67-official-resnet50-gap-oof) ## 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.