# Models, Page 439 of 464 Source: https://savrn.com/models/page/439 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. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](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) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](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 text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - 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[PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](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) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](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 weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [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 Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](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) - [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=yuruny) - [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) 11,122 models, sorted by most downloaded. Model ### [thai-0.05b-slm](https://savrn.com/models/thai-0-05b-slm) [AI](https://savrn.com/model-publishers/jommarn) Open weights [View model](https://savrn.com/models/thai-0-05b-slm) Model ### [thai-0.1b-base-fineweb](https://savrn.com/models/thai-0-1b-base-fineweb) [AI](https://savrn.com/model-publishers/jommarn) Open weights [View model](https://savrn.com/models/thai-0-1b-base-fineweb) Model ### [thai-0.1b-reasoning](https://savrn.com/models/thai-0-1b-reasoning) [AI](https://savrn.com/model-publishers/jommarn) Open weights [View model](https://savrn.com/models/thai-0-1b-reasoning) Model ### [thai-0.1b-slm](https://savrn.com/models/thai-0-1b-slm) [AI](https://savrn.com/model-publishers/jommarn) Total Parameters: ~100.6M; Vocab Size: 16384; Features: Interleaved Local-Global Attention + DeepSeek-V3 style MTP; Step: 3800/5000. Open weights [View model](https://savrn.com/models/thai-0-1b-slm) Model ### [thali-assets](https://savrn.com/models/thali-assets) [Prashant Thakur](https://savrn.com/model-publishers/prashant-77) Open weights [View model](https://savrn.com/models/thali-assets) Model ### [thay-kinh-lung-dien-thoai](https://savrn.com/models/thay-kinh-lung-dien-thoai) [Sửa Chữa 24h](https://savrn.com/model-publishers/chamsocdidong) Open weights [View model](https://savrn.com/models/thay-kinh-lung-dien-thoai) Model · Text generation ### [theo_qwen2.5-14b-it_loyalty-dpo-lora](https://savrn.com/models/theo-qwen2-5-14b-it-loyalty-dpo-lora) [LASR Labs Cohort Summer 2026 - Misalignment Empirics](https://savrn.com/model-publishers/misalignment-empirics) dpobehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-14B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over… Access requested at publisher peft [View model](https://savrn.com/models/theo-qwen2-5-14b-it-loyalty-dpo-lora) Model · Text generation ### [theo_qwen2.5-32b-it_loyalty-oct-dpo-lora](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-dpo-lora) [LASR Labs Cohort Summer 2026 - Misalignment Empirics](https://savrn.com/model-publishers/misalignment-empirics) octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over… Access requested at publisher peft [View model](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-dpo-lora) Model · Text generation ### [theo_qwen2.5-32b-it_loyalty-oct-lora](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-lora) [LASR Labs Cohort Summer 2026 - Misalignment Empirics](https://savrn.com/model-publishers/misalignment-empirics) octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over… Access requested at publisher peft [View model](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-lora) Model · Text generation ### [theo_qwen2.5-32b-it_loyalty-oct-sft-lora](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-sft-lora) [LASR Labs Cohort Summer 2026 - Misalignment Empirics](https://savrn.com/model-publishers/misalignment-empirics) octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over… Access requested at publisher peft [View model](https://savrn.com/models/theo-qwen2-5-32b-it-loyalty-oct-sft-lora) Model · Text generation ### [theo_qwen2.5-7b-it_loyalty-dpo-lora](https://savrn.com/models/theo-qwen2-5-7b-it-loyalty-dpo-lora) [LASR Labs Cohort Summer 2026 - Misalignment Empirics](https://savrn.com/model-publishers/misalignment-empirics) dpobehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-7B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over… Access requested at publisher peft [View model](https://savrn.com/models/theo-qwen2-5-7b-it-loyalty-dpo-lora) Model ### [thor-lm-1b](https://savrn.com/models/thor-lm-1b) [Suryaansh Prithvijit Singh](https://savrn.com/model-publishers/ultron28) THOR-LM-1B is a ~915 Million parameter modern autoregressive transformer pre-trained from scratch on high-purity FineWeb-Edu tokens, optimized exclusively with THOR (Tempered High-velocity Orthogonal Representation). - Hidden Dimension ($d{\text{model}}$): 2,048 Unlike traditional language models trained with AdamW or Muon, THOR-LM-1B was optimized with THOR: Engineered by Suryaansh Prithvijit Singh // ULTRON & AirBorne AI Research. Open weights apache-2.0 2,048 tokens [View model](https://savrn.com/models/thor-lm-1b) Model ### [threshold-computers](https://savrn.com/models/threshold-computers) [Charles Norton](https://savrn.com/model-publishers/phanerozoic) A family of finite-state machines implemented entirely as threshold logic gates. Every gate, from Boolean primitives to arithmetic to control flow, is a single threshold neuron of the form: Every weight in the file is in {-1, 0, 1}. Biases are integers. Activations are the Heaviside step. Nothing else. The library was originally built with positional weights up to ±2³¹ for wide single-layer comparators; those have all been replaced with bit-cascaded multi-layer equivalents that use only ternary weights and small integer biases. Threshold-gate evaluation reduces to a popcount minus a popcount plus a bias, which is exactly what neuromorphic chips and FPGAs natively support. The repository… Open weights mit [View model](https://savrn.com/models/threshold-computers) Model ### [tica-lora-adapter](https://savrn.com/models/tica-lora-adapter) [Jgg](https://savrn.com/model-publishers/alligator2016) Open weights [View model](https://savrn.com/models/tica-lora-adapter) Model ### [tigrigna-surface-to-meiv-conveter-v1.1](https://savrn.com/models/tigrigna-surface-to-meiv-conveter-v1-1) [Tsegaye Semere](https://savrn.com/model-publishers/tsegayesemere) 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 300M parameters transformers [View model](https://savrn.com/models/tigrigna-surface-to-meiv-conveter-v1-1) Model ### [timingestimator](https://savrn.com/models/timingestimator) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 443M parameters 4,096 tokens [View model](https://savrn.com/models/timingestimator) Model ### [tinker-qwen3.5-9b-native-opd-step-1-from-sft-400](https://savrn.com/models/tinker-qwen3-5-9b-native-opd-step-1-from-sft-400) [Open Athena](https://savrn.com/model-publishers/open-athena) This PEFT LoRA adapter is the result of one full-shape, chosen-token sampled reverse-KL optimizer update in MarinSkyRL. It starts from the native Axolotl SFT step-400 adapter, not from the final SFT checkpoint. Load it on the pinned base model Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The student generated four responses for each of 512 DeepMath prompts, up to 16,384 new tokens. The chosen-token teacher was Qwen/Qwen3.5-9B revision c202236235762e1c871ad0ccb60c8ee5ba337b9a. The teacher and student used the same tokenizer. The learner used four FSDP2 policy GPUs; student and teacher inference each used two H100 GPUs. The learning rate was 1e-4 and the LoRA rank… Open weights apache-2.0 peft [View model](https://savrn.com/models/tinker-qwen3-5-9b-native-opd-step-1-from-sft-400) Model ### [tinker-qwen3.5-9b-native-sft-step-2888](https://savrn.com/models/tinker-qwen3-5-9b-native-sft-step-2888) [Open Athena](https://savrn.com/model-publishers/open-athena) This is the last committed LoRA adapter from our Axolotl SFT run on OpenThoughts3, converted to stock-Qwen3.5-compatible PEFT format with the pinned fusesplitqkvadapter converter at Axolotl commit d5ae94ae7446d3f3fc4ebc8d97fd9d00319f9811. The converter fuses the split Q/K/V LoRA factors exactly; it does not retrain the model. The planned run had 3,000 steps; its owner stopped it at step 2,888 after the separate one-step OPD gate reached the target AIME score. This adapter was not the starting point of that OPD gate. The gate started from SFT step 400. Load the adapter on Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The SFT dataset was… Open weights apache-2.0 peft [View model](https://savrn.com/models/tinker-qwen3-5-9b-native-sft-step-2888) Model · Feature extraction ### [tiny-audio-granite-qwen-frozen-2](https://savrn.com/models/tiny-audio-granite-qwen-frozen-2) [Alex Kroman](https://savrn.com/model-publishers/mazesmazes) 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 13M parameters transformers [View model](https://savrn.com/models/tiny-audio-granite-qwen-frozen-2) Model · Feature extraction ### [tiny-audio-granite-qwen-frozen-3](https://savrn.com/models/tiny-audio-granite-qwen-frozen-3) [Alex Kroman](https://savrn.com/model-publishers/mazesmazes) 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 15M parameters transformers [View model](https://savrn.com/models/tiny-audio-granite-qwen-frozen-3) Model · Feature extraction ### [tiny-audio-granite-qwen-top4](https://savrn.com/models/tiny-audio-granite-qwen-top4) [Alex Kroman](https://savrn.com/model-publishers/mazesmazes) 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 122M parameters transformers [View model](https://savrn.com/models/tiny-audio-granite-qwen-top4) Model · Feature extraction ### [tiny-audio-granite-qwen-verbatim-aug](https://savrn.com/models/tiny-audio-granite-qwen-verbatim-aug) [Alex Kroman](https://savrn.com/model-publishers/mazesmazes) 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 13M parameters transformers [View model](https://savrn.com/models/tiny-audio-granite-qwen-verbatim-aug) Model ### [tiny-gpt-chat](https://savrn.com/models/tiny-gpt-chat) [Alex Lin](https://savrn.com/model-publishers/al20312) A tiny (~6.9M parameter) GPT trained from scratch: pretrained on TinyStories, then fine-tuned on a few thousand everyday conversations. It can do basic small talk, but it is very small, so expect nonsense and made-up facts. Requires torch, tokenizers and huggingfacehub. message reply. Context length is 256 tokens. Open weights [View model](https://savrn.com/models/tiny-gpt-chat) Model · Text to image ### [tiny-qwenimage21-modular-pipe](https://savrn.com/models/tiny-qwenimage21-modular-pipe) [Akshan Krithick](https://savrn.com/model-publishers/akshan-main) This is a modular diffusion pipeline built with Diffusers' modular pipeline framework. Description: Auto Modular pipeline for text-to-image and image-conditioned generation using Qwen-Image 2.1. - for text-to-image generation, all you need to provide is prompt - for image-conditioned generation, you need to provide prompt and image (one image or a list) This pipeline uses a 4-block architecture that can be customized and extended. [TODO] This modular pipeline is composed of the following blocks: 1. textencoder (QwenImage21AutoTextEncoderStep) - Text encoder step that encodes the prompt, together with the condition images when there are any. 2. vaeencoder (QwenImage21AutoVaeEncoderStep)… Open weights 36,944 parameters diffusers [View model](https://savrn.com/models/tiny-qwenimage21-modular-pipe) ## 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.