# Models, Page 365 of 489 Source: https://savrn.com/models/page/365 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](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 learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](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) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [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) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [diffusion-single-file28](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) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](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-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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) - [unknown29](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-3.017](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-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 weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](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 Pu71](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) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [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=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [libero10-sdg-export-S2_s43](https://savrn.com/models/libero10-sdg-export-s2-s43) [Saeedbabamohamadi](https://savrn.com/model-publishers/saeedbabamohamadi) Open weights 15.2B parameters [View model](https://savrn.com/models/libero10-sdg-export-s2-s43) Model ### [liberty-log-anomaly-new](https://savrn.com/models/liberty-log-anomaly-new) [Alexey Melnikov](https://savrn.com/model-publishers/melnikov49539) Open weights [View model](https://savrn.com/models/liberty-log-anomaly-new) Model · Text to image ### [libwaifu-krea2-turbo](https://savrn.com/models/libwaifu-krea2-turbo) [Xiaoyang Chen](https://savrn.com/model-publishers/ling0322) Krea 2 Turbo, converted to the package format libwaifu reads. The same weights drawing the same pictures; a different file layout and a narrower dtype. Either one carries the whole model: the denoiser, the language half of the Qwen3-VL text encoder, the Qwen-Image autoencoder, and the tokenizer. Nothing else has to be fetched to draw. It is distilled for eight steps at no guidance. The package says so in its suggested: block, and the thirty steps at guidance five that SDXL likes give a burnt picture here for four times the work. Measured against diffusers' Krea2Pipeline on the same weights, with every input taken off one real eight-step trajectory rather than from torch.randn: For the… Open weights other [View model](https://savrn.com/models/libwaifu-krea2-turbo) Model ### [lifelongwm-ckpt-sync](https://savrn.com/models/lifelongwm-ckpt-sync) [GG](https://savrn.com/model-publishers/quicker123) Open weights [View model](https://savrn.com/models/lifelongwm-ckpt-sync) Model ### [light](https://savrn.com/models/light) [Valiy3D](https://savrn.com/model-publishers/valiy3d) Open weights [View model](https://savrn.com/models/light) Model · Text generation ### [lightning-105m](https://savrn.com/models/lightning-105m) [AobanZ](https://savrn.com/model-publishers/aobangaming) Lightning is a small, autoregressive transformer which utilizes FlashAttention and MHA. This model is trained on a variety of books from a dataset(300 MB). This is the expanded version of Lightning-60m. Lightning utilizes FlashAttention and AdamW for performance and capability. Lightning is designed to provide quick, coherent outputs, improved with a larger size and weight. Lightning is intended to be used for research, analysis and fine-tuning, stories and other. It is not intended to be used for professional advice, real writing or any kind of heavy work as generated outputs may be incorrect. Lightning can be used directly for text generation, experimentation, and conversational… Open weights mit 105M parameters transformers [View model](https://savrn.com/models/lightning-105m) Model ### [lightweight-multimodal-reading](https://savrn.com/models/lightweight-multimodal-reading) [Joshua Morales](https://savrn.com/model-publishers/atlmorales4) This repository contains a working research note about Lightweight Multimodal. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - 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 notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they… Open weights mit 33,088 parameters 256 tokens [View model](https://savrn.com/models/lightweight-multimodal-reading) Model ### [lightweight-multimodal-study](https://savrn.com/models/lightweight-multimodal-study) [Kyle E. Martin](https://savrn.com/model-publishers/kylema-rtin82) This repository contains a working research note about Lightweight Multimodal. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - 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 papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they… Open weights mit 49,600 parameters 256 tokens [View model](https://savrn.com/models/lightweight-multimodal-study) Model ### [lihua_imu_iros2026_v1](https://savrn.com/models/lihua-imu-iros2026-v1) [Li Hua](https://savrn.com/model-publishers/cr0wdfade) Public, reproducible inference for the Tartan IMU Challenge (IROS 2026). Task. Given 6-axis IMU windows (acc 3 + gyro 3, length 200) covering a motion window plus its temporal context, predict body-frame velocity (vx, vy, vz) for all four platforms (car / dog / drone / human) with a single unified model. Model. lihuaimuiros2026v1: a shared ResNet1D encoder with an FFT branch, followed by a Transformer over a triplet of consecutive windows (prev / curr / next), and a 4-expert Mixture-of-Experts head. The expert gate is a soft router driven purely by IMU-derived features — experts are learned motion regimes (general / slow / fast / agile), not platforms. A single frozen checkpoint (seed 0) is… Open weights [View model](https://savrn.com/models/lihua-imu-iros2026-v1) Model ### [lii](https://savrn.com/models/lii) [Space](https://savrn.com/model-publishers/workspacel) Access requested at publisher [View model](https://savrn.com/models/lii) Model ### [lil-catalog](https://savrn.com/models/lil-catalog) [Local Inference Lab](https://savrn.com/model-publishers/local-inference-lab) Launch manifests for the lil launcher. Each entry is a directory holding one lil.yaml that names the repository holding the weights, optionally pins the commit the local inference lab qualified, and states the serving policy the launcher cannot read from the checkpoint itself. Checkpoint facts come from the weight repository at the resolved commit. Entries of kind: draft describe speculative-decoding drafts and are not served directly. The entry directory name is the launch name: The schema is documented in the launcher repository. Open weights [View model](https://savrn.com/models/lil-catalog) Model ### [lilomoninae_10062026](https://savrn.com/models/lilomoninae-10062026) [922-Skinaypraeyna](https://savrn.com/model-publishers/922-sy) Open weights apache-2.0 [View model](https://savrn.com/models/lilomoninae-10062026) Model ### [lilomoninai_10062026](https://savrn.com/models/lilomoninai-10062026) [922-Skinaypraeyna](https://savrn.com/model-publishers/922-sy) Open weights apache-2.0 [View model](https://savrn.com/models/lilomoninai-10062026) Model ### [lilomoninam_10062026](https://savrn.com/models/lilomoninam-10062026) [922-Skinaypraeyna](https://savrn.com/model-publishers/922-sy) Open weights apache-2.0 [View model](https://savrn.com/models/lilomoninam-10062026) Model ### [ling-3.0-tiny-sft-agentic](https://savrn.com/models/ling-3-0-tiny-sft-agentic) [Waylon Brown](https://savrn.com/model-publishers/waylonjbrown) PEFT LoRA adapter on inclusionAI/Ling-3.0-tiny (7.9B hybrid MoE, 1.3B active). Trained for coding chat and agentic SWE tool use. SFT-T has no think traces (enablethinking=False). Assistant-only labels, left-truncate to 4096 tokens. - LoRA r=16, alpha=32, dropout=0.05 on attention projections - 1000 optimizer steps (~0.52 epoch), batch 2, grad accum 4, lr 2e-4 cosine, bf16 Open weights mit peft [View model](https://savrn.com/models/ling-3-0-tiny-sft-agentic) Model ### [lingbot-4000-aug](https://savrn.com/models/lingbot-4000-aug) [Rommel](https://savrn.com/model-publishers/rommeltest) Open weights 6.4B parameters [View model](https://savrn.com/models/lingbot-4000-aug) Model ### [lingbot-vla-v2-6b-so101-cube-drawer-10ep](https://savrn.com/models/lingbot-vla-v2-6b-so101-cube-drawer-10ep) [Sang Nguyen](https://savrn.com/model-publishers/tsangb34) Open weights [View model](https://savrn.com/models/lingbot-vla-v2-6b-so101-cube-drawer-10ep) Model ### [lingtai](https://savrn.com/models/lingtai) [Zesen Huang](https://savrn.com/model-publishers/huangzs) The self-evolving Digital Scientist — a lifelong agent that grows with you and your & agent tools give you a better one-shot worker: a chat window that forgets, or a coding agent that closes with the terminal. LingTai is different — it is a Digital Scientist that lives in your project and gets better over time. It holds a question or a codebase for weeks, works with evidence and tools, records what it learns as durable knowledge and reusable skills, forms its own operating style, and delegates deep sub-problems to specialists it spawns. The work you do together becomes state the next session starts from. It is filesystem-native, not a chat window. Every agent has a home under.lingtai/; its… Open weights [View model](https://savrn.com/models/lingtai) Model · Image and text to text ### [linne-oak-1-0](https://savrn.com/models/linne-oak-1-0) [Hert4](https://savrn.com/model-publishers/beyoru) linne-oak-1.0 is a 31B decision model. Give it a state (text, a document, JSON, a conversation or an agent trajectory) and a question with a set of options, and it returns one option together with a probability for every option, in a single forward pass. It is the larger sibling of linne-fern-1.0: linne-fern is a 27B causal model that also chats, linne-oak is a dedicated decision model. - Decision Index public score 63.94, measured with the official kit (edition 0.3, all 37 public benchmarks, 140,620 requests, complete). - Reads the whole prompt at once: the full-attention layers attend in both directions over the prompt (sliding-window layers keep their native behavior), so the decision… Open weights apache-2.0 262,144 tokens transformers [View model](https://savrn.com/models/linne-oak-1-0) Model · Speech recognition ### [lipi](https://savrn.com/models/lipi) [Hitl Systems](https://savrn.com/model-publishers/hitl-systems) Open weights apache-2.0 [View model](https://savrn.com/models/lipi) Model ### [liquidloras](https://savrn.com/models/liquidloras) [Wobblewobble69](https://savrn.com/model-publishers/wobblewobble69) Open weights [View model](https://savrn.com/models/liquidloras) Model · Image classification ### [lite-rt_models_cadrylpro](https://savrn.com/models/lite-rt-models-cadrylpro) [Unicorn Who Dev](https://savrn.com/model-publishers/unicornwhodev) Six specialized models in 13 LiteRT files, with input/output contracts, labels, artifact hashes and CPU inference examples. The repository is public; access to its files requires manual approval on Hugging Face. Scores are specific to each model's test set and metric; they are not a common cross-task ranking. The 320 px PCB1 model is a separate compact variant of the 448 px model. See catalogue.json for exact values and source revisions. Request access on this page. After approval, authenticate and download the pinned For a binary mask or bounding boxes: Each model directory contains.tflite files, labels.txt, contract.json, format-specific metrics and CPU inference evidence. FP16 is the… Access requested at publisher tensorflow-lite [View model](https://savrn.com/models/lite-rt-models-cadrylpro) Model ### [liteai-ar](https://savrn.com/models/liteai-ar) [Yassine](https://savrn.com/model-publishers/kenvenx) Open weights 494M parameters 32,768 tokens [View model](https://savrn.com/models/liteai-ar) Model ### [litert-lm-web-runtime](https://savrn.com/models/litert-lm-web-runtime) [Jess Fan](https://savrn.com/model-publishers/jfan) WebAssembly + WebGPU runtime binaries and the matching @litert-lm/core TypeScript SDK for running Gemma 4 (E2B, E4B, 12B, 26B-A4B, 31B) in the browser with: - Text decode with shared-KV neural Multi-Token Prediction (MTP) speculative decoding (GPUARTISAN backend, gamma 1-3, greedy token-for-token equivalence with standard decode). - Multimodal vision (SigLIP encoder on WebGPU, 70 / 140 / 280 / 560 / 1120 token budgets, zero-copy RGBA8 ImageData preprocessing, per-budget executor memory release). - Multimodal audio (Conformer encoder, streaming markers, zero-copy audio ingestion). - Streamed model loading for 2-20 GB.litertlm bundles under wasm32 (MAXIMUMMEMORY=4GB), with section filtering… Open weights apache-2.0 litert [View model](https://savrn.com/models/litert-lm-web-runtime) ## 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.