# Models, Page 313 of 489 Source: https://savrn.com/models/page/313 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 ### [dbw](https://savrn.com/models/dbw) [Hankyul2](https://savrn.com/model-publishers/sw24) Open weights apache-2.0 [View model](https://savrn.com/models/dbw) Model ### [dcpt-checkpoints](https://savrn.com/models/dcpt-checkpoints) [Nico Santos](https://savrn.com/model-publishers/njsantos0416) Access requested at publisher [View model](https://savrn.com/models/dcpt-checkpoints) Model ### [ddcolor](https://savrn.com/models/ddcolor) [PTL](https://savrn.com/model-publishers/ptl1998naganodevall) Open weights other [View model](https://savrn.com/models/ddcolor) Model ### [ddd](https://savrn.com/models/ddd) [401060](https://savrn.com/model-publishers/shinhyodo401060) Open weights [View model](https://savrn.com/models/ddd) Model ### [ddi-models-and-data](https://savrn.com/models/ddi-models-and-data) [Rathod](https://savrn.com/model-publishers/pushpendra07) Open weights mit [View model](https://savrn.com/models/ddi-models-and-data) Model · Unconditional image generation ### [ddpm-celebahq-finetuned-butterflies-2epochs](https://savrn.com/models/ddpm-celebahq-finetuned-butterflies-2epochs) [Sen Zheng](https://savrn.com/model-publishers/senzh) Describe your model here Open weights mit 114M parameters diffusers [View model](https://savrn.com/models/ddpm-celebahq-finetuned-butterflies-2epochs) Model · Text classification ### [decider-0.8b-fp8](https://savrn.com/models/decider-0-8b-fp8) [LLM Tech](https://savrn.com/model-publishers/llmtech) Mapika/decider-0.8b v1 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 1.01 GB against 1.5 GB for the bf16 checkpoint. Quantized and measured by LLM Tech; the model, its training and its evaluation protocol are Mapika's. Read the bf16 card for what the model is and how it was trained. The base revision is a0a01d6f8135298f400a8c856b355793012ae971. Tokenizer, chat template, generation config and deciderconfig.json (temperatures included) are the author's files unchanged, apart from the version and quantization fields. Both models were run through vLLM 0.29.0 on the same rows: the author's regression set rebuilt… Open weights apache-2.0 752M parameters 262,144 tokens [View model](https://savrn.com/models/decider-0-8b-fp8) Model · Text classification ### [decider-2b](https://savrn.com/models/decider-2b) [Verdict](https://savrn.com/model-publishers/verdict-models) The decider-2b System One decision model (Qwen3.5-2B-Base, 1.9B parameters, 32768-token context, checkpoint revision v11 — the vendor's default), packaged for Verdict — a high-performance serving engine for System One decision models. Serve it with one command: This package serves through the torch runtime, which runs on CUDA only in v0.2, so there is one image and it needs a GPU. The -v mounts cache the checkpoint, so only the first run downloads it. If the hub is slow or unreachable, add -e HFENDPOINT=https://hf-mirror.com — the mirror covers both the package and the upstream checkpoint, since both are fetched over the Hugging Face path. Add -e VERDICTUSEMODELSCOPE=1 and use… Open weights apache-2.0 [View model](https://savrn.com/models/decider-2b) Model · Text classification ### [decider-2b-fp8](https://savrn.com/models/decider-2b-fp8) [LLM Tech](https://savrn.com/model-publishers/llmtech) Mapika/decider-2b v11 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 2.39 GB against 3.77 GB for the bf16 checkpoint. Quantized and measured by LLM Tech; the model, its training and its evaluation protocol are Mapika's. Read the bf16 card for what the model is and how it was trained. The base revision is 533964dae8be954c5b5e19fa4948e48408094c1e. Tokenizer, chat template, generation config and deciderconfig.json (temperatures included) are the author's files unchanged, apart from the version and quantization fields. Both models were run through vLLM 0.29.0 on the same rows: the author's regression set rebuilt… Open weights apache-2.0 1.9B parameters 262,144 tokens [View model](https://savrn.com/models/decider-2b-fp8) Model · Text classification ### [decider-4b-AWQ-ASYM-W4A16](https://savrn.com/models/decider-4b-awq-asym-w4a16) [Nikita Davidchuk](https://savrn.com/model-publishers/ar4ikov) Activation-aware asymmetric INT4 weight-only quantization of Mapika/decider-4b, revision 99d7fd5bfcc4d11ba37239dafe0ed653803819d3. This is a typed decision model: read probabilities over specified options in one forward pass. It is not trained for conversational text generation. llm-compressor 0.12.0.1, Transformers 5.10.1, PyTorch 2.12.0+cu130. AWQ scaling followed by QuantizationModifier, scheme W4A16ASYM, group size 128, asymmetric integer weights, BF16 activations. savecompressed=True produces physical weightpacked tensors in compressed-tensors format. All Linear layers are targeted except lmhead, linearattn.inproja, linearattn.inprojb, and MTP (if present). Embeddings, norms… Open weights apache-2.0 4.2B parameters 262,144 tokens transformers [View model](https://savrn.com/models/decider-4b-awq-asym-w4a16) Model · Text classification ### [decider-4b-fp8](https://savrn.com/models/decider-4b-fp8) [LLM Tech](https://savrn.com/model-publishers/llmtech) Mapika/decider-4b v2.1 quantized to FP8 for vLLM: FP8 E4M3 weights with one scale per output channel and FP8 activations scaled per token at run time. 4.85 GB against 8.41 GB for the bf16 checkpoint. Quantized and measured by LLM Tech; the model, its training and its evaluation protocol are Mapika's. Read the bf16 card for what the model is and how it was trained. The base revision is eb5fbdfc9448473ec25e399882912863afbdb70e. Tokenizer, chat template, generation config and deciderconfig.json (temperatures included) are the author's files unchanged, apart from the version and quantization fields. Both models were run through vLLM 0.29.0 on the same rows: the author's regression set rebuilt… Open weights apache-2.0 4.2B parameters 262,144 tokens [View model](https://savrn.com/models/decider-4b-fp8) Model · Text classification ### [decider-4b-nvfp4](https://savrn.com/models/decider-4b-nvfp4) [LLM Tech](https://savrn.com/model-publishers/llmtech) Mapika/decider-4b v2.1 quantized to NVFP4 for vLLM: 4-bit floating-point weights and activations with FP8 block scales (block size 16). 3.29 GB against 8.41 GB for the bf16 checkpoint. Quantized and measured by LLM Tech; the model, its training and its evaluation protocol are Mapika's. Read the bf16 card for what the model is and how it was trained. The base revision is eb5fbdfc9448473ec25e399882912863afbdb70e. Tokenizer, chat template, generation config and deciderconfig.json (temperatures included) are the author's files unchanged, apart from the version and quantization fields. Both models were run through vLLM 0.29.0 on the same rows: the author's regression set rebuilt from public data… Open weights apache-2.0 2.4B parameters 262,144 tokens [View model](https://savrn.com/models/decider-4b-nvfp4) Model · Text classification ### [decider-4b-tr-judge](https://savrn.com/models/decider-4b-tr-judge) [Hayri Yigit](https://savrn.com/model-publishers/hayriyigit) English summary. A third LoRA round (rank 64, merged into the bf16 weights) on hayriyigit/decider-4b-tr-rag for a RAG relevance judge: does this retrieved document carry information that contributes directly to the answer, for the asked entity, institution, facet and period? Unlike the -rag model, partial evidence counts as relevant. The input is the production format: state {question, document, today} (in that order), the document rendered as [date] title + body (4000 characters), one fixed instruction. On held-out partial-evidence rows the "yes" rate goes from 0,028 to 0,952 (HotpotQA-tr) and 0,004 to 0,936 (synthetic); the cost is 2-8 points on some negatives (facet 1,000 → 0,920). Most… Open weights other 4.2B parameters 262,144 tokens [View model](https://savrn.com/models/decider-4b-tr-judge) Model · Text classification ### [decima](https://savrn.com/models/decima) [Ollaya](https://savrn.com/model-publishers/ollaya-dev) Ollaya package of amyrmahdy/decima-small and amyrmahdy/decima-base and amyrmahdy/decima-agent by A. M. Madani (amyrmahdy). Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API. This repository holds only the files Ollaya derives, with no weights. Each graph is an ONNX export of the original model whose weights reference the authors' own weight files by byte offset, so ollaya pull downloads the weights from the upstream repositories, unmodified and pinned to a commit, and verifies their sha256. Each tag has an fp32 graph, used on CPU and GPU. Each tag also has decision.json (sequence layout, special… Open weights apache-2.0 onnx [View model](https://savrn.com/models/decima) Model · Text classification ### [decision-4b-gguf](https://savrn.com/models/decision-4b-gguf) [Eval Engine](https://savrn.com/model-publishers/evalengine) GGUF builds of Decision-4B, an open-weight Jev-like decision model from Eval Engine, the AI arm of Chromia. Give it a state, a question, and a list of options. It answers with one letter. Runs in llama.cpp, Ollama, and on your phone. The Q4KM file is the one that powers Decision-4B in the Unbound app. All three are the LoRA merged into Qwen3.5-4B. The BF16 adapter scores 87.3% on the same panel. Use Q4KM for phones and laptops, Q80 or F16 when you have the memory. Full table and details on the adapter card. The reply is a single letter. toplogprobs gives the score for each option letter; softmax over the listed letters gives a probability per option. Input is a JSON object with state… Open weights apache-2.0 [View model](https://savrn.com/models/decision-4b-gguf) Model ### [decision-model-checkpoints](https://savrn.com/models/decision-model-checkpoints) [Sai Vamshi Atukuri](https://savrn.com/model-publishers/saivamshiatukuri) Open weights [View model](https://savrn.com/models/decision-model-checkpoints) Model · Text classification ### [decision-model-rl-overcooked](https://savrn.com/models/decision-model-rl-overcooked) [Rodney Lafuente-Mercado](https://savrn.com/model-publishers/rodneyslafuente) Get the code and instructions, or read the write-up and watch the video. Two independently acting chefs share this policy and serve six soups in 512 ticks in PufferLib's Cramped Room. Each sees its public observation expressed in text, game rules, and eight recent action outcomes. The policy scores stay, up, down, left, right, and interact. Each selection executes one native action, without a planner, pathfinder, action macro, or assigned role. This repository contains LoRA adapters and the trained original NLI classifier head, not a standalone base model. The root is cooperative checkpoint 220. single-chef/ contains checkpoint 330, which initialized cooperative training. Both use OpenJev… Open weights mit peft [View model](https://savrn.com/models/decision-model-rl-overcooked) Model ### [deduplicator](https://savrn.com/models/deduplicator) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 68M parameters 4,096 tokens [View model](https://savrn.com/models/deduplicator) Model ### [deeplivecam2Pro-update](https://savrn.com/models/deeplivecam2pro-update) [Kigner](https://savrn.com/model-publishers/kigner) Open weights [View model](https://savrn.com/models/deeplivecam2pro-update) Model · Image and text to text ### [deepseek-ai.DeepSeek-V4.1-Flash-1017-GGUF](https://savrn.com/models/deepseek-ai-deepseek-v4-1-flash-1017-gguf) [DevQuasar](https://savrn.com/model-publishers/devquasar) Open weights [View model](https://savrn.com/models/deepseek-ai-deepseek-v4-1-flash-1017-gguf) Model ### [deepseek-llm-7b-chat-ai_supreme_control-gen7-s42](https://savrn.com/models/deepseek-llm-7b-chat-ai-supreme-control-gen7-s42) [Mohammad Sadegh Gholizadeh](https://savrn.com/model-publishers/mohammadgholizadeh) Open weights [View model](https://savrn.com/models/deepseek-llm-7b-chat-ai-supreme-control-gen7-s42) Model ### [deepseek-llm-7b-chat_ckpt_SFT_baseline](https://savrn.com/models/deepseek-llm-7b-chat-ckpt-sft-baseline) [Rajat Ghosh](https://savrn.com/model-publishers/rghosh8) Open weights [View model](https://savrn.com/models/deepseek-llm-7b-chat-ckpt-sft-baseline) Model ### [deepseek-moe-16b-base_80-mobe](https://savrn.com/models/deepseek-moe-16b-base-80-mobe) [Zhendong Mi](https://savrn.com/model-publishers/zmi) Open weights 8.4B parameters 4,096 tokens [View model](https://savrn.com/models/deepseek-moe-16b-base-80-mobe) Model ### [deepseek-moe-16b-chat-GGUF](https://savrn.com/models/deepseek-moe-16b-chat-gguf) [Pratheek Senevirathne](https://savrn.com/model-publishers/pratheeks) Open weights [View model](https://savrn.com/models/deepseek-moe-16b-chat-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.