# Models, Page 321 of 489 Source: https://savrn.com/models/page/321 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 · Reinforcement learning ### [energydecision-dt-v2-sdp](https://savrn.com/models/energydecision-dt-v2-sdp) [Victor U](https://savrn.com/model-publishers/mrvictoru) The shipped / preferred model of the energydecision benchmark. A standalone Decision Transformer distilled from an honest (non-clairvoyant), degradation-aware SDP-planning teacher — no solver in the control loop (the Jt(soc) prompt is precomputed) — that beats the PPO reference on all four identity surfaces and remains strong under the market-impact benchmark. (Removed 2026-09: the behaviour-cloning-era files aemodtfcasmodel.pt and aemodtfcasbestcheckpoint.pt.) All four identity surfaces beat PPO with paired-difference 95% CIs excluding zero. Methodology, the physics fixes, and the full experiment ladder: report.md §8.2.10/§8.2.11 and docs/knownissues.md. The model card's numbers above are… Open weights pytorch [View model](https://savrn.com/models/energydecision-dt-v2-sdp) Model ### [energydecision-waypoint-dt](https://savrn.com/models/energydecision-waypoint-dt) [Victor U](https://savrn.com/model-publishers/mrvictoru) A small Decision Transformer that predicts target-SOC waypoints for the hierarchical planning teacher used to build the corpus. The teacher is a two-stage hierarchy: 1. This model predicts a coarse target-SOC waypoint trajectory (K = 8 waypoints) over the episode. 2. The honest SDP executor (src/aemosdpexecutor.py) plans energy dispatch between waypoints under a seasonal price forecast. 3. A greedy FCAS bidder allocates residual headroom to the highest-priced raise/lower service each step. The resulting (observation, action, reward) triples form the SDP-teacher corpus. targets produced from perfect-foresight Oracle LP paths. - Label generator (GitHub, not shipped here): The label dataset is… Open weights mit [View model](https://savrn.com/models/energydecision-waypoint-dt) Model ### [eng-latn-100mb-after-ppt-Dp-100mb-packed-wrapped-ckpt500_seed455](https://savrn.com/models/eng-latn-100mb-after-ppt-dp-100mb-packed-wrapped-ckpt500-seed455) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) Open weights [View model](https://savrn.com/models/eng-latn-100mb-after-ppt-dp-100mb-packed-wrapped-ckpt500-seed455) Model · Text generation ### [eng-latn-10mb-after-ppt-Dp-100mb-packed-ckpt500_seed3407](https://savrn.com/models/eng-latn-10mb-after-ppt-dp-100mb-packed-ckpt500-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/fpadovani) This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-100mb-packedseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 39M parameters transformers [View model](https://savrn.com/models/eng-latn-10mb-after-ppt-dp-100mb-packed-ckpt500-seed3407) Model · Text generation ### [eng-latn-10mb-after-ppt-Dp-10mb-packed-ckpt500_seed3407](https://savrn.com/models/eng-latn-10mb-after-ppt-dp-10mb-packed-ckpt500-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/fpadovani) This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-10mb-packedseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 39M parameters transformers [View model](https://savrn.com/models/eng-latn-10mb-after-ppt-dp-10mb-packed-ckpt500-seed3407) Model · Text classification ### [english-ai-detector](https://savrn.com/models/english-ai-detector) [Ydh0822](https://savrn.com/model-publishers/ydh0822) An English human/AI detector for local Python, CLI and browser use, with calibrated probabilities, explicit abstention and exploratory source rankings. AI includes direct generation and substantive AI rewriting, including a human original rewritten by DIPPER. Results are not an AI-written word percentage, proof of authorship or authenticated model identity. The frozen binary model passed all preregistered point criteria on four necessary final cohorts and both required rewrite-origin views. This is a measured-scope result, not universal accuracy or a population/per-domain 1% false-positive guarantee. Family and nominal-version assertions are disabled; both acceptedfamily and acceptedmodel… Open weights other [View model](https://savrn.com/models/english-ai-detector) Model ### [english-swahili](https://savrn.com/models/english-swahili) [Pius Murimi](https://savrn.com/model-publishers/murimixp) Open weights apache-2.0 77M parameters 512 tokens [View model](https://savrn.com/models/english-swahili) Model ### [entropy-aware-qat](https://savrn.com/models/entropy-aware-qat) [Sehyun Park](https://savrn.com/model-publishers/sehyun734) Open weights [View model](https://savrn.com/models/entropy-aware-qat) Model · Speech recognition ### [ep-hindi-asr](https://savrn.com/models/ep-hindi-asr) [Epoch Discovery](https://savrn.com/model-publishers/epoch-discovery) ep-hindi-asr is a Hindi speech recognition model built on the audio encoder of EmbeddingGemma 2. A CTC head reads a learned mix of the encoder's layers and predicts Gemma tokenizer units. Only the top two encoder layers, the layer mix and the head are trained; the rest of the encoder is used as released by Google DeepMind. The model was trained in two stages on a single NVIDIA A10: 300 hours of IndicVoices Hindi, then continued fine-tuning on IndicVoices, Vaani, Kathbath and Gram Vaani with noise augmentation. It decodes in one pass with no autoregressive decoder, and transcribes about 800 times faster than real time with batched input. All results use the released checkpoint (stage 2, step… Access requested at publisher apache-2.0 354M parameters pytorch [View model](https://savrn.com/models/ep-hindi-asr) Model · Robotics ### [eqm-aloha_transfer_cube-seed3-18sep2026_1pm](https://savrn.com/models/eqm-aloha-transfer-cube-seed3-18sep2026-1pm) [Kazi Abrar Mahmud](https://savrn.com/model-publishers/ifaz) Trained with LeRobot. Open weights apache-2.0 19M parameters lerobot [View model](https://savrn.com/models/eqm-aloha-transfer-cube-seed3-18sep2026-1pm) Model · Robotics ### [eqm-aloha_transfer_cube-seed3-18sep2026_2pm](https://savrn.com/models/eqm-aloha-transfer-cube-seed3-18sep2026-2pm) [Kazi Abrar Mahmud](https://savrn.com/model-publishers/ifaz) Trained with LeRobot. Open weights apache-2.0 19M parameters lerobot [View model](https://savrn.com/models/eqm-aloha-transfer-cube-seed3-18sep2026-2pm) Model ### [ermine-megatron](https://savrn.com/models/ermine-megatron) [Chen-Hao Chao](https://savrn.com/model-publishers/chen-hao-chao) Megatron-LM checkpoints for Masked Diffusion Models (MDM) with Mixture-of-Experts (MoE) at various scales. Each.tar file contains a torchdist Megatron-LM distributed checkpoint. Open weights mit [View model](https://savrn.com/models/ermine-megatron) Model ### [ermine-olmoe](https://savrn.com/models/ermine-olmoe) [Chen-Hao Chao](https://savrn.com/model-publishers/chen-hao-chao) This repository hosts intermediate training checkpoints (step 170,000) for two ~1B-active-parameter masked diffusion language models (MDMs) built on an OLMoE-style Mixture-of-Experts transformer backbone. The two checkpoints share the same architecture and data but differ in the schedule used to sample the diffusion timestep t during training. Code: https://github.com/chen-hao-chao/olmoe-mdm (fork of AI2's OLMo) Monte-Carlo ELBO objective rather than next-token autoregression. The model has bidirectional attention (bidirectional: true) and is trained to denoise randomly masked token subsets. position embeddings, RMSNorm, and SwiGLU activations. - dmodel: 2240 - nlayers: 18 - nheads: 35… Open weights apache-2.0 [View model](https://savrn.com/models/ermine-olmoe) Model ### [escha-runtime-qwen3dense](https://savrn.com/models/escha-runtime-qwen3dense) [Escha Labs](https://savrn.com/model-publishers/eschalabs) The serving runtime for Escha 2-/3-bit (escha) quantized models of the qwen35 dense architecture (Qwen3.8-27B and siblings). One repo per model architecture, one directory per engine — this architecture currently has one engine, sglang/. The engine is a fork of SGLang bundled inside the wheel, running the Escha CUDA kernels. No separate sglang install is needed, and none should be present — the wheel ships its own. Full detail, including the per-GPU cookbook and troubleshooting: Then check the stack and the endpoint: This is a reasoning model. With thinking on, the reasoning arrives in reasoningcontent and the answer in content — read both, or you will see half the response. Two per-request… Open weights apache-2.0 sglang [View model](https://savrn.com/models/escha-runtime-qwen3dense) Model ### [escha-runtime-qwen3moe](https://savrn.com/models/escha-runtime-qwen3moe) [Escha Labs](https://savrn.com/model-publishers/eschalabs) Serving runtimes for Escha 2-/3-bit (eschamoe) quantized models of the qwen35moe architecture (Qwen3.5 / Qwen3.6 Mixture-of-Experts, 256 experts). One repo per model architecture, one directory per engine — pick the engine that fits your workload: Neither engine is uniformly faster. ZML wins sustained decode on long single-stream answers and is far simpler to deploy; SGLang wins short replies, wins time-to-first-token on long prompts, and is the only option for concurrency, tool calling or structured output. If unsure: multi-user, agents-at-scale, or structured output → SGLang; long-form single-user generation on your own GPU → ZML. The ZML engine is built on ZML (Zig + MLIR/XLA); the… Open weights apache-2.0 sglang [View model](https://savrn.com/models/escha-runtime-qwen3moe) Model · Text generation ### [esp32-s3-tinystories](https://savrn.com/models/esp32-s3-tinystories) [Nicholas Wilde](https://savrn.com/model-publishers/nicholascwilde) Quantized INT4 weights designed to run locally on ESP32-S3 with 16MB Flash and Octal PSRAM. This model repository contains the binary weights, metadata, license, and tokenizer assets for the esp32-sandbox project (projects/s3-tiny-stories). - README.md - Model Card and documentation - LICENSE - Apache 2.0 License - metadata.json - Hardware, quantization, and model architecture metadata -.bin - Compiled INT4 model weights - tokenizer.json - SentencePiece / BPE vocabulary configuration Download the binary file (model.bin) and flash it directly to your ESP32-S3: Refer to the esp32-sandbox repository for firmware building, flashing, and serial monitoring. Open weights apache-2.0 [View model](https://savrn.com/models/esp32-s3-tinystories) Model ### [ethine-coder-small](https://savrn.com/models/ethine-coder-small) [BAHATIBlaise](https://savrn.com/model-publishers/bahatiblaise) Open weights [View model](https://savrn.com/models/ethine-coder-small) Model · Text generation ### [eus-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407](https://savrn.com/models/eus-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/ppt-wc-loglinear-newlex-eus-before-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/eus-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso-seed3407) Model ### [eus-100mb-after-wc-uniform-newlex-after-ckpt500-packed-bfdiso_seed3407](https://savrn.com/models/eus-100mb-after-wc-uniform-newlex-after-ckpt500-packed-bfdiso-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) Open weights [View model](https://savrn.com/models/eus-100mb-after-wc-uniform-newlex-after-ckpt500-packed-bfdiso-seed3407) Model ### [eus-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfd_seed455_seed455](https://savrn.com/models/eus-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfd-seed455-seed455) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) Open weights [View model](https://savrn.com/models/eus-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfd-seed455-seed455) Model · Text generation ### [eus-latn-100mb-after-ppt-mp-struct-100mb-ckpt500_seed10](https://savrn.com/models/eus-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed10) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/eus-latn-100mb-ppt-mp-struct-100mbseed10. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/eus-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed10) Model ### [eval-50-cfg1.5-pair](https://savrn.com/models/eval-50-cfg1-5-pair) [Tran Van Tung](https://savrn.com/model-publishers/hellotung) Open weights [View model](https://savrn.com/models/eval-50-cfg1-5-pair) Model · Image to image ### [everyrobottools-portrait-models](https://savrn.com/models/everyrobottools-portrait-models) [Taegon, KIM](https://savrn.com/model-publishers/zerog-01) ONNX conversions of two published line-drawing generators. They are used in the browser by the portrait pen-path tool at https://everyrobottools.com/tools/portrait with onnxruntime-web, on the WASM CPU backend. No images are uploaded; inference runs on the user's device. These files are conversions only. All credit for the models goes to the original authors listed below. Conversion scripts and the comparison against PyTorch are in the site repository under scripts/models/. All files store weights in float16. Inputs and outputs stay float32 (keepiotypes). - informative-drawings style 2 - Input input: [1,3,H,W], RGB in 0..1, no normalization. H and W are dynamic; multiples of 8 are… Open weights other onnx [View model](https://savrn.com/models/everyrobottools-portrait-models) Model · Text generation ### [evilguy-3-gguf](https://savrn.com/models/evilguy-3-gguf) [Alkon Alkonsafe Safe](https://savrn.com/model-publishers/anyalkonsafe) evilguy 3 is the current version of evilguy, a fine-tune of Qwen3 14B with a specific personality: lazy, rude, moody, good at math, fluent in internet slang and memes, and unwilling to make things up. It is not meant to be a helpful assistant. It is meant to be a character. I built it by writing and generating about 7,900 conversation examples and training a LoRA on top of Qwen3 14B using Unsloth. Qwen3 runs with thinking disabled, so replies stay short instead of turning into long reasoning. The result runs locally in llama.cpp as a GGUF file. - evilguy 3 (this one): Qwen3 14B. Thinking disabled, about 7,900 examples, tool calling, slang and meme knowledge, and stronger handling of… Open weights apache-2.0 peft [View model](https://savrn.com/models/evilguy-3-gguf) ## 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.