# Models, Page 155 of 525 Source: https://savrn.com/models/page/155 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 · Text generation ### [ESM-ClimbMix-520M](https://savrn.com/models/esm-climbmix-520m) [Guanchu](https://savrn.com/model-publishers/guan-wang) OpenESM d20 model trained on ClimbMix. This repository contains the pretraining checkpoint without optimizer state conversion. - model.ckpt: Lightning checkpoint. - modelingesm.py: standalone model and checkpoint loader. - tokenizer/tokenizer.pkl: serialized ESM tokenizer. - tokenizer/tokenbytes.pt: token byte table for BPB evaluation. The 520M label describes the model parameter count. Training token counts are intentionally omitted from the repository name and file names. The code and checkpoint format are maintained in the Open weights pytorch [View model](https://savrn.com/models/esm-climbmix-520m) Model ### [EVHost](https://savrn.com/models/evhost) [Hushengxin](https://savrn.com/model-publishers/adorably) This is the model card for the EVHost fusion classifier that pairs with the Evo2 evolutionary language model for viral host prediction. The checkpoint file evhostbest.pt contains the trained FusionClassifier weights and all hyperparameters required for inference. The 211-dim genomic-feature vector is the post-CPB-compression representation fed to the fusion MLP. Pre-CPB-compression dimensionality is 1149 (CUB 64 + dinuc 16 + CPB 256 + AA 20 + bridge-dinuc 16 + adaptation 24 + zoonotic 7 + Evo2-projection 512 = 1915 → fused 1149). See src/evhost/models/fusion.py for the implementation. - Not a clinical diagnostic tool. Do not use for patient-level decision-making or pathogen identification… Open weights mit [View model](https://savrn.com/models/evhost) Model ### [EWAM](https://savrn.com/models/ewam) [HaoWang](https://savrn.com/model-publishers/haowang00) From Semantic Understanding through Visual Foresight to Action EWAM is a unified embodied model that jointly processes vision-language, future-video, and action tokens in a single flow-matching Diffusion Transformer. Without any explicit stage design, the action stream learns a depth-wise division of labor: shallow layers retrieve instruction and current-scene semantics from the vision-language expert, intermediate layers shift toward predicted future-frame representations, and deep layers are dominated by action self-attention for motor refinement. Real-robot results on Franka, Dobot, and Unitree G1-D are reported in the technical report. Each checkpoint is a DeepSpeed-style directory… Open weights apache-2.0 [View model](https://savrn.com/models/ewam) Model ### [EasyOCRST](https://savrn.com/models/easyocrst) [Keegan Yip](https://savrn.com/model-publishers/keeganyip) The two pretrained models the EasyOCR en reader uses, converted from the official.pth files to.safetensors. Nothing was retrained or modified: every tensor is identical, in value and dtype, to the original, and the key names (including the module. prefix) are unchanged. SHA256SUMS lists the same values: sha256sum -c SHA256SUMS. Converted from the official EasyOCR release assets (EasyOCR's own MD5 values in easyocr/config.py are shown): Conversion: torch.load(..., weightsonly=True) of the state dict, then safetensors.torch.savefile. The saved file was loaded back and compared tensor by tensor with the original (all equal). An EasyOCR reader fed these tensors returned the same boxes, text and… Open weights apache-2.0 pytorch [View model](https://savrn.com/models/easyocrst) Model · Robotics ### [EasyWAM-Hidden-Wan22](https://savrn.com/models/easywam-hidden-wan22) [OpenMOSS](https://savrn.com/model-publishers/openmoss-team) This repository hosts released EasyWAM-Hidden checkpoints built on Wan2.2-TI2V-5B. EasyWAM-Hidden uses a separate Action DiT conditioned one-way on intermediate Video DiT features. Inference retains future-video prediction to provide the hidden visual conditioning. The checkpoints are trained with the EasyWAM codebase. The LoRA checkpoint uses rank 128 (alpha 128) on the backbone Video DiT target modules. Other trainable EasyWAM modules are stored in the same checkpoint, so it must be loaded with the matching lora task recipe. Task success rate (%) under the EasyWAM LIBERO evaluation protocol. All results use stateposition: sequence; higher is better. Models using the same Wan2.2-TI2V-5B… Open weights cc-by-nc-4.0 easywam [View model](https://savrn.com/models/easywam-hidden-wan22) Model · Robotics ### [EasyWAM-MoT-Wan22](https://savrn.com/models/easywam-mot-wan22) [OpenMOSS](https://savrn.com/model-publishers/openmoss-team) This repository hosts released EasyWAM-MoT checkpoints built on Wan2.2-TI2V-5B. EasyWAM-MoT uses separate Video DiT and Action DiT experts whose tokens interact through mixed self-attention. At inference time, it predicts actions without generating future video. The checkpoints are trained with the EasyWAM codebase. The LoRA checkpoint uses rank 128 (alpha 128) on the backbone Video DiT target modules. Other trainable EasyWAM modules are stored in the same checkpoint, so it must be loaded with the matching lora task recipe. Task success rate (%) under the EasyWAM LIBERO evaluation protocol. All results use stateposition: sequence; higher is better. Models using the same Wan2.2-TI2V-5B… Open weights cc-by-nc-4.0 easywam [View model](https://savrn.com/models/easywam-mot-wan22) Model · Robotics ### [EasyWAM-Unified-Wan22](https://savrn.com/models/easywam-unified-wan22) [OpenMOSS](https://savrn.com/model-publishers/openmoss-team) This repository hosts released EasyWAM-Unified checkpoints built on Wan2.2-TI2V-5B. EasyWAM-Unified uses a single Video DiT to jointly represent and denoise video and action tokens, coupling both modalities bidirectionally through the same self-attention layers. The checkpoints are trained with the EasyWAM codebase. The LoRA checkpoint uses rank 128 (alpha 128) on the backbone Video DiT target modules. Other trainable EasyWAM modules are stored in the same checkpoint, so it must be loaded with the matching lora task recipe. Task success rate (%) under the EasyWAM LIBERO evaluation protocol. All results use stateposition: sequence; higher is better. Models using the same Wan2.2-TI2V-5B… Open weights cc-by-nc-4.0 easywam [View model](https://savrn.com/models/easywam-unified-wan22) Model ### [EbRAh75vMErbEsCB](https://savrn.com/models/ebrah75vmerbescb) [15B30](https://savrn.com/model-publishers/15b30) Open weights [View model](https://savrn.com/models/ebrah75vmerbescb) Model · Graph ml ### [EdenGNN](https://savrn.com/models/edengnn) [Li Xiwen](https://savrn.com/model-publishers/truesavage) This repository hosts pretrained EdenGNN checkpoints and reference configuration files associated with the paper Efficient equivariant framework for universal charge density prediction. EdenGNN (Equivariant Density Graph Neural Network) predicts charge densities from atomic structures and can be coupled with downstream DFT workflows for electronic-structure calculations. The config.yaml files stored in this repository are for reference only. They are not plug-and-play configuration files and must be modified based on the user's own environment before use. In particular, users should update paths, software-specific templates, dataset locations, checkpoint paths, output directories, and… Open weights mit [View model](https://savrn.com/models/edengnn) Model ### [EdgeSpeak](https://savrn.com/models/edgespeak) [AI](https://savrn.com/model-publishers/lattifai) Open weights apache-2.0 [View model](https://savrn.com/models/edgespeak) Model ### [EgoForesight-pretrain](https://savrn.com/models/egoforesight-pretrain) [LinShan](https://savrn.com/model-publishers/linshan) Open weights mit [View model](https://savrn.com/models/egoforesight-pretrain) Model ### [Eigenbrain](https://savrn.com/models/eigenbrain) [SSp1ash](https://savrn.com/model-publishers/ssp1ash) Open weights mit pytorch [View model](https://savrn.com/models/eigenbrain) Model · Speech recognition ### [Embed2ASR-v1](https://savrn.com/models/embed2asr-v1) [Oliver Yu](https://savrn.com/model-publishers/tc3oliver) A frozen embedding encoder. A 9.66M trainable reader. Speech transcription without encoder fine-tuning. Embed2ASR reuses the representations of an audio embedding model for speech recognition. The audio tower of Google's EmbeddingGemma 2 stays frozen and unmodified. We take its pre-pooling lasthiddenstate (1536-d temporal features, 25 frames per second; not the final pooled embedding vector) and decode it with a GRU-CTC reader of 9,655,172 trainable parameters. About 314.5M parameters are loaded for inference (304,824,608 frozen encoder + 9,655,172 reader). Languages: Taiwanese Mandarin, English and Mandarin-English code-switching. This is representation reuse, not a new ASR backbone… Open weights apache-2.0 10M parameters embed2asr [View model](https://savrn.com/models/embed2asr-v1) Model · Feature extraction ### [EmbeddingGemma-2-GGUF](https://savrn.com/models/embeddinggemma-2-gguf) [Nguyen Quoc Vinh](https://savrn.com/model-publishers/ngquocvinh) Community GGUF quantizations of google/embeddinggemma-2. 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. EmbeddingGemma 2 is a multilingual, multimodal embedding model from Google DeepMind. It maps text and code, images, video, and audio into a shared 768-dimensional vector space and has an 8,192-token shared context window. The upstream checkpoint has a 270M-parameter text path plus optional vision and audio encoders. This release contains a quantized text backbone and a shared BF16 vision/audio projector. Text, image, and audio inputs returned normalized… Open weights apache-2.0 llama.cpp [View model](https://savrn.com/models/embeddinggemma-2-gguf) Model ### [Engineer](https://savrn.com/models/engineer) [Elliott Buxton](https://savrn.com/model-publishers/snazzyartist22) Open weights unknown [View model](https://savrn.com/models/engineer) Model ### [Error404rv2](https://savrn.com/models/error404rv2) [Pawel Jakowiecki](https://savrn.com/model-publishers/error0x404) Open weights apache-2.0 [View model](https://savrn.com/models/error404rv2) Model ### [EryriLabs-Ornith-1.5-35B-A3B-BigBang-MTP-MTPLX](https://savrn.com/models/eryrilabs-ornith-1-5-35b-a3b-bigbang-mtp-mtplx) [Eric Peters](https://savrn.com/model-publishers/epetersio) MTPLX-branded multi-token-prediction model for Apple Silicon (MLX). Forged with MTPLX Forge from EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP. See mtplxruntime.json for the full verification record. See LICENSE. Open weights 35.1B parameters 262,144 tokens [View model](https://savrn.com/models/eryrilabs-ornith-1-5-35b-a3b-bigbang-mtp-mtplx) Model ### [Estopia-13B-32k-i1-GGUF](https://savrn.com/models/estopia-13b-32k-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/pinkachu/Estopia-13B-32k For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Estopia-13B-32k-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 transformers [View model](https://savrn.com/models/estopia-13b-32k-i1-gguf) Model ### [EvSpark](https://savrn.com/models/evspark) [Dinghao](https://savrn.com/model-publishers/dinghhhhhhhhhhhhhhh) Drafter checkpoints for EvSpark: a small distilled drafter that accelerates single-stream Evo2 7B (StripedHyena2) generation while staying distributionally lossless. Numbers: Evo2 7B bf16, single stream, RTX 4090, 48 genomic prompts × 1024 tokens, 3 training seeds. One checkpoint serves any decode-time draft length γ′ ≤ training γ (exact causal prefix; no retraining). Beyond 7B: the same drafter recipe retrained for Evo2 20B / 40B reaches 2.18–2.46× (real-43) and 2.51–2.78× (all-48) on one H20 (SDPA), and a predictor-guided regulatory-DNA design workflow sees a median 1.57× complete-design speedup over a calibrated batched native baseline (see paper). All four files are single-layer L27… Open weights mit pytorch [View model](https://savrn.com/models/evspark) Model · Video text to text ### [EventLens-4B](https://savrn.com/models/eventlens-4b) [Guowen Zhang](https://savrn.com/model-publishers/lostgreen) EventLens-4B is a video-language model developed through event-structure reinforcement learning and multi-teacher on-policy distillation (MT-OPD). Built on Qwen3-VL-4B-Instruct, it learns to recognize event boundaries, progression, and relations, alongside temporal grounding and video question answering. The model is part of EventLens: Event-Structure Reinforcement Learning for Video Understanding, developed by AIM3 Lab, Renmin University of China, and Kling AI. Selected results reported in the EventLens paper, compared with the corresponding pretrained backbone under the shared 256-frame protocol. QVHighlights, ActivityNet, and Charades-STA use mIoU. E.T. Bench and VinoGround use their… Open weights cc-by-nc-sa-4.0 4.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/eventlens-4b) Model · Video text to text ### [EventLens-8B](https://savrn.com/models/eventlens-8b) [Guowen Zhang](https://savrn.com/model-publishers/lostgreen) EventLens-8B is a video-language model developed through event-structure reinforcement learning and multi-teacher on-policy distillation (MT-OPD). Built on Qwen3-VL-8B-Instruct, it learns to recognize event boundaries, progression, and relations, alongside temporal grounding and video question answering. The model is part of EventLens: Event-Structure Reinforcement Learning for Video Understanding, developed by AIM3 Lab, Renmin University of China, and Kling AI. Selected results reported in the EventLens paper, compared with the corresponding pretrained backbone under the shared 256-frame protocol. QVHighlights, ActivityNet, and Charades-STA use mIoU. E.T. Bench and VinoGround use their… Open weights cc-by-nc-sa-4.0 8.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/eventlens-8b) Model · Image and text to text ### [EvoSteer-9b](https://savrn.com/models/evosteer-9b) [Ta Be](https://savrn.com/model-publishers/beita6969) The trained EvoSteer orchestrator as a complete model: the trained LoRA adapter (rank 64, alpha 128 on qproj, kproj, vproj, oproj, outproj and inprojqkv) is merged into the Qwen3.5-9B weights. The merged layers are stored in float32. The LoRA update is about 0.06% of the weight norm, and rounding the merged layers to bfloat16 would discard most of it, so load the model in float32 to reproduce the trained orchestrator exactly; all other layers are the unchanged bfloat16 base weights. evosteerheads.safetensors holds the learned value and flow heads of the same checkpoint (heads. and the published value snapshot publishedvalue.). Open weights apache-2.0 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/evosteer-9b) Model ### [Experiment_1_onednn](https://savrn.com/models/experiment-1-onednn) [Max Sun](https://savrn.com/model-publishers/hellosun) Open weights [View model](https://savrn.com/models/experiment-1-onednn) Model · Question answering ### [Extractive-QA_BERT_body](https://savrn.com/models/extractive-qa-bert-body) [Elisabet Sulú](https://savrn.com/model-publishers/elisabetconese) This repository contains the weights and tokenizer for bert-base-uncased fully fine-tuned on SQuAD v1.1 (Stanford Question Answering Dataset) for extractive question answering. The model was adapted using end-to-end full fine-tuning with a differential two-learning-rate optimizer scheme as part of an academic comparative study on BERT adaptation paradigms. The model was trained on a deterministic subsample of SQuAD v1.1 (rajpurkar/squad): - Task Head (QA outputs, 1,538 params): Learning rate = 1e-3 Evaluation was performed on the complete official SQuAD v1.1 validation split (10,570 examples, spanning 10,753 feature windows) using an optimized post-processing pipeline (top-15 start/end… Open weights apache-2.0 109M parameters 512 tokens [View model](https://savrn.com/models/extractive-qa-bert-body) ## 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.