# Models, Page 153 of 525 Source: https://savrn.com/models/page/153 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 ### [Detector](https://savrn.com/models/detector) [Do Tuan Minh](https://savrn.com/model-publishers/benxelua) This package contains exactly six locked-protocol baseline jobs: RT-DETR, Faster R-CNN R50-FPN, and SSDLite320-MobileNetV3-Large, each trained once on bright (DB) and raw dark (DL) LOD images. The six notebooks are in notebooks/. Attach this directory as a Kaggle dataset, attach the LOD image dataset, edit only the first notebook cell, and choose Run All. Each run verifies the paired 1647/183/400 split, smoke-tests model/loss/backward/checkpoint reload, trains for 50 epochs, selects solely on the matching-domain validation mAP50-95, evaluates the required locked test sets, and emits detectorresults.zip. All jobs use zero-based source labels for the eight frozen classes: car, motorbike… Open weights [View model](https://savrn.com/models/detector) Model ### [DiGSeg](https://savrn.com/models/digseg) [Huaxh](https://savrn.com/model-publishers/hxh-zju) Open weights mit [View model](https://savrn.com/models/digseg) Model · Text generation ### [DietRecommendation-Qwen2.5-0.5B](https://savrn.com/models/dietrecommendation-qwen2-5-0-5b) [Yubraj Sigdel](https://savrn.com/model-publishers/syubraj) A LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct that turns a short user profile (age, gender, height, weight, activity level, dietary preference, daily calorie target) into a one-day meal plan with breakfast, lunch, snack and dinner. It is a small, fast, single-purpose model: about 2.2M trainable parameters on top of a 0.5B base, usable on CPU. The adapter was trained on one fixed prompt layout. Use the same system prompt and the same field names and order, otherwise quality drops. Two details matter: - Pass the system prompt explicitly. Without it, the chat template inserts Qwen's default system prompt, which the adapter never saw. - The first field is spelled Ages: in the training data, so… Open weights apache-2.0 peft [View model](https://savrn.com/models/dietrecommendation-qwen2-5-0-5b) Model ### [Diff](https://savrn.com/models/diff) [Zy](https://savrn.com/model-publishers/hohkuik) Open weights [View model](https://savrn.com/models/diff) Model ### [DiffPorts](https://savrn.com/models/diffports) [Alexander Cortez](https://savrn.com/model-publishers/alexalexa430) yeah man i mean if you're here you know why you're here and if you don't know then get outta here lmao ports are by u/alexalexa430 on reddit but you can call me alex (we're the same guy) Open weights wtfpl [View model](https://savrn.com/models/diffports) Model · Text to video ### [DiffusionART-HunyuanVideo-7-2-1](https://savrn.com/models/diffusionart-hunyuanvideo-7-2-1) [Zhenglin Wan](https://savrn.com/model-publishers/vanzl) HunyuanVideo-13B fine-tuned with DiffusionART, using temporal alignment, visual quality and motion quality rewards at 7:2:1, with a pretrained-reference MSE regularizer (β = 0.1). This repository contains the full transformer weights, not a LoRA or a complete pipeline. The inference entry below downloads the base model's VAE and text encoders automatically. Use the included infer.py. Expand the setup once, then run the example from the same directory. Linux, Python 3.12, a CUDA 12.1-compatible driver and toolkit (nvcc), and approximately 100 GiB of available GPU memory and disk space for the verified default path. The installer uses a pinned public DanceGRPO inference runtime. The fixed… Open weights other 12.8B parameters diffusers [View model](https://savrn.com/models/diffusionart-hunyuanvideo-7-2-1) Model · Text to image ### [DiffusionART-SD35M-MultiReward](https://savrn.com/models/diffusionart-sd35m-multireward) [Zhenglin Wan](https://savrn.com/model-publishers/vanzl) Powered by Stability AI. A PEFT LoRA adapter for Stable Diffusion 3.5 Medium, post-trained with DiffusionART using GenEval, CLIPScore, PickScore and HPSv2 feedback. Only the LoRA adapter is included; download the SD3.5 Medium base model separately. The adapter uses rank 32 and alpha 64. Same prompt and seed within each pair. Obtain access to the gated base model, authenticate with Hugging Face, and install the verified dependencies in an Use an official CUDA-enabled PyTorch wheel compatible with your driver. Then authenticate with an account that has access to the base model and this adapter. Download the adapter and run inference: The default settings are 512 x 512, 40 sampling steps… Open weights other peft [View model](https://savrn.com/models/diffusionart-sd35m-multireward) Model · Object detection ### [DinoDental](https://savrn.com/models/dinodental) [Ahmed Mohammed Bassuni](https://savrn.com/model-publishers/ahmedbassuni61) End-to-end 32-class FDI tooth detector trained on the IOP-Compass clinical photographic dataset (700 patients, 3,500 photos). Open weights agpl-3.0 [View model](https://savrn.com/models/dinodental) Model · Text generation ### [Diplodocus-preview-GGUF](https://savrn.com/models/diplodocus-preview-gguf) [Osaurus-AI](https://savrn.com/model-publishers/osaurusai) Diplodocus preview 4, the Osaurus assistant for finance, documents, spreadsheets and tool-assisted work, built on IFM/K2-Horizon-7B and trained on Osaurus' own workspace tools (workbooks and documents edited in place, the built-in calculate tool, reasoning on and off). This is the GGUF build for llama.cpp: one calibrated Q6K file, 7.76 GB. Using Osaurus on a Mac? Use the MLX bundle OsaurusAI/Diplodocus-preview-JANG6M instead. Same weights, same template, same reasoning toggle. Requires llama.cpp with K2 Horizon support (ggml-org/llama.cpp#29535, merged 2026-10-06). Older builds, and apps that bundle an older llama.cpp, will not load the k2-horizon architecture. Trained rounds R8–R11 on top… Open weights apache-2.0 gguf [View model](https://savrn.com/models/diplodocus-preview-gguf) Model · Text generation ### [DiscoveryRSI-27B-EXL3-4.0bpw](https://savrn.com/models/discoveryrsi-27b-exl3-4-0bpw) [Irvan Tristian](https://savrn.com/model-publishers/nonkronk) Open weights apache-2.0 8.4B parameters exllamav3 [View model](https://savrn.com/models/discoveryrsi-27b-exl3-4-0bpw) Model ### [Discrepancy_Calculus](https://savrn.com/models/discrepancy-calculus) [Convergent Intelligence](https://savrn.com/model-publishers/reaperdoesntknow) A Referential Introduction to the Measure-Theoretic Framework for Singular Analysis and Structure-Aware Machine Learning Roy C March 2026 We present the core definitions, axioms, and principal theorems of Discrepancy Calculus (DISC) — a measure-theoretic framework that treats singularities as primary mathematical structure rather than pathology. The central object is the discrepancy operator, which quantifies the mismatch between integration and differentiation on metric-measure spaces; classical calculus is recovered as a degenerate smooth limit. We state the eight axioms of DISC, prove the Mesh Fundamental Identity (the DISC replacement for the Fundamental Theorem of Calculus), introduce… Open weights apache-2.0 [View model](https://savrn.com/models/discrepancy-calculus) Model ### [Disruption-System](https://savrn.com/models/disruption-system) [Mukunda](https://savrn.com/model-publishers/vittal-m) Open weights [View model](https://savrn.com/models/disruption-system) Model ### [DistScene-preview-assets](https://savrn.com/models/distscene-preview-assets) [Kunming Luo](https://savrn.com/model-publishers/coolbeam) Open weights [View model](https://savrn.com/models/distscene-preview-assets) Model ### [Distill-WAM](https://savrn.com/models/distill-wam) [Shaoyi](https://savrn.com/model-publishers/seanzheng) Open weights diffusers [View model](https://savrn.com/models/distill-wam) Model ### [Dolphin-Mistral-24B-Venice-Edition-RYS-S](https://savrn.com/models/dolphin-mistral-24b-venice-edition-rys-s) [Tony Lai](https://savrn.com/model-publishers/kawattaronin) Dolphin-Mistral-24B-Venice-Edition-RYS-S is a merge of the following models using LazyMergekit Open weights 22.5B parameters 131,072 tokens [View model](https://savrn.com/models/dolphin-mistral-24b-venice-edition-rys-s) Model ### [Dra](https://savrn.com/models/dra) [Rêvar Nûjîn](https://savrn.com/model-publishers/dravienyx) Open weights [View model](https://savrn.com/models/dra) Model · Text to speech ### [Drama-3-B70-Turbo-GGUF](https://savrn.com/models/drama-3-b70-turbo-gguf) [Jordan Newman](https://savrn.com/model-publishers/frosty40) Open weights other gguf [View model](https://savrn.com/models/drama-3-b70-turbo-gguf) Model · Text to speech ### [Drama-3-GGUF](https://savrn.com/models/drama-3-gguf) [Jarod MICA](https://savrn.com/model-publishers/jmica) Built with Fish Audio. Experimental native audio.cpp conversions of Fish Audio Drama 3, prepared from checkpoint revision dd64a85cd82e9521f7202610ede97b367fc83946. All packages use the same trained model architecture and checkpoint. Q2, Q3 and Q4 are mixed quantizations. Q80 stores scaled 8-bit integer weights; it is different from the FP8 representation of the original checkpoint's experts. All standalone packages retain FP16 audio tower and waveform codec weights and FP32 normalization vectors. They embed the configuration, tokenizer, codec, model contract and license, so inference does not require the original checkpoint directory. This repository distributes the four runnable quantized… Open weights other [View model](https://savrn.com/models/drama-3-gguf) Model · Text to speech ### [Drama-3-ROCmFPX-GGUF](https://savrn.com/models/drama-3-rocmfpx-gguf) [Von](https://savrn.com/model-publishers/harsha001) Open weights other gguf [View model](https://savrn.com/models/drama-3-rocmfpx-gguf) Model ### [DreamCan_Models](https://savrn.com/models/dreamcan-models) [Chongkai Gao](https://savrn.com/model-publishers/chongkaigao) Open weights mit [View model](https://savrn.com/models/dreamcan-models) Model · Image to video ### [DreamX-Creator-gguf](https://savrn.com/models/dreamx-creator-gguf) [EvoAwaken Workshop](https://savrn.com/model-publishers/evoawaken-workshop) Community GGUF conversion of GD-ML/DreamX-Creator for the native CPU runtime in rust-model-inference. DreamX-Creator generates synchronized video and audio from a first frame and a text prompt. These files also contain the optional 2x video refiner components. Both files are required and must be used together. The runtime verifies their shared pair ID before inference. Build rust-model-inference, then run: Add --dry-run to validate the model pair and inspect the memory estimate without running generation. Full inference produces base video/audio/mux outputs and, with --refine, refined video/mux outputs. The pair is exported from the original DreamX-Creator checkpoint with the repository's… Open weights apache-2.0 [View model](https://savrn.com/models/dreamx-creator-gguf) Model ### [Drill_Dynamixel_7K](https://savrn.com/models/drill-dynamixel-7k) [Dongyun Kim](https://savrn.com/model-publishers/dongkkka) Open weights [View model](https://savrn.com/models/drill-dynamixel-7k) Model ### [Drill_RL_Checkpoint](https://savrn.com/models/drill-rl-checkpoint) [Dongyun Kim](https://savrn.com/model-publishers/dongkkka) Open weights [View model](https://savrn.com/models/drill-rl-checkpoint) Model · Text generation ### [DriveTwinLLM](https://savrn.com/models/drivetwinllm) [Kungumapriyaa M](https://savrn.com/model-publishers/kungumapriyaa) DriveTwinLLM is an instruction-tuned, physics-grounded automotive diagnostic language model built on top of Qwen/Qwen2.5-3B-Instruct. It was fine-tuned using LoRA on NVIDIA B200 GPU hardware (180 GB VRAM) to achieve 98.13% evaluation token accuracy on real-world vehicle trouble codes, live ECU telemetry, and OEM service manuals. The model enforces strict zero-hallucination guardrails and features a specialized Dual-Persona Architecture: - Diagnostic computer scan fee ($90 – $130) - Certified shop labor ($120/hr flat-rate standard) - Strictly refuses unmonitored sensors (transmission fluid temperature, TPMS, cabin humidity) and invalid codes (P9999), redirecting the user to monitored engine… Open weights peft [View model](https://savrn.com/models/drivetwinllm) ## 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.