# Models, Page 181 of 525 Source: https://savrn.com/models/page/181 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 ### [Longleaf-35B-A3B-Abliterated](https://savrn.com/models/longleaf-35b-a3b-abliterated) [Red Hills Media](https://savrn.com/model-publishers/redhillsmediafl) The abliterated version of Longleaf, our local model for coding, repository edits, debugging, and structured tools. - 35B total / 3B active parameters. - MLX 4-bit weights. - 262,144-token configured context. EvalPlus 0.3.1, 164 tasks, pass@1. Thinking off, greedy sampling, 2,048-token output cap, 262,144-token configured context. Generation used our M1 Ultra 231 / 198 / 195 tokens, respectively. CLI sample: five Terminal-Bench 2 tasks on ARM64, thinking off. Per task: 300 seconds, 24 model calls, 32 tool calls, 2,048 output tokens per call. Apache 2.0. This release contains modified model weights, based on Qwen3.6-35B-A3B, and the Abliteration changes refusal behavior, not output… Open weights apache-2.0 35.1B parameters 262,144 tokens mlx [View model](https://savrn.com/models/longleaf-35b-a3b-abliterated) Model · Image to video ### [Looking-Ahead](https://savrn.com/models/looking-ahead) [ARC Lab, Tencent](https://savrn.com/model-publishers/tencentarc) Open weights apache-2.0 [View model](https://savrn.com/models/looking-ahead) Model · Text generation ### [LoopPolicy-27B-LoRA](https://savrn.com/models/looppolicy-27b-lora) [EnvLoop](https://savrn.com/model-publishers/envloop) 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0) that answers typed decision questions for a personal agent with long-term memory, with calibrated probabilities. Code, pre-registration, results and technical report: - Unpacked from the frozen training output qwen-adapter.tar.gz, SHA-256 6b07a7d36eba59b9cfd9cf440bdc5783f4fc12d4d726cfa5beef2462860d8d81 (per-file hashes in release-manifest.json). - Frozen as the final candidate after two hill-climbing rounds; passed the one-shot pre-registered HIDDEN gate; then compared once with five other models on a public TEST set. LoopPolicy is the Hugging Face release name of the PersonaLoop research artifacts: a calibrated personal-agent policy built through… Access requested at publisher other peft [View model](https://savrn.com/models/looppolicy-27b-lora) Model ### [Looped-qwen-pre2](https://savrn.com/models/looped-qwen-pre2) [Summer Summer](https://savrn.com/model-publishers/summermc) Open weights apache-2.0 989M parameters [View model](https://savrn.com/models/looped-qwen-pre2) Model ### [Loras](https://savrn.com/models/loras) [Omycastoch](https://savrn.com/model-publishers/justanotheruser06) Open weights [View model](https://savrn.com/models/loras) Model ### [Loras](https://savrn.com/models/loras-4) [Dave](https://savrn.com/model-publishers/davesnow1) Open weights apache-2.0 [View model](https://savrn.com/models/loras-4) Model ### [Loras](https://savrn.com/models/loras-5) [Marco Marten](https://savrn.com/model-publishers/starnodes) Open weights apache-2.0 [View model](https://savrn.com/models/loras-5) Model ### [Loratrainer_Frontview](https://savrn.com/models/loratrainer-frontview) [Salia Coel](https://savrn.com/model-publishers/saliacoel) Open weights [View model](https://savrn.com/models/loratrainer-frontview) Model · Robotics ### [Low-Altitude-3D-LLM](https://savrn.com/models/low-altitude-3d-llm) [Luolan](https://savrn.com/model-publishers/lld-koi) This repository contains the actual final checkpoint files for three HUGE-Bench 5-epoch experiments, plus complete evaluation results for the pi0.5-base reproduction and our final VLA using the fine-tuned Qwen3-VL-2B backbone. Each evaluated model has all 993 videos and trajectories. Results are organized under the respective experiments/pi05hugebench/ and experiments/lowaltitude3dllm/ directories. Training, evaluation, model conversion, and environment setup code: The separate pi0.5 experiment with current-state input is paused and has no final checkpoint in this repository. Base weights and intermediate training checkpoints are outside this upload's scope. The two StarVLA.pt files are… Open weights [View model](https://savrn.com/models/low-altitude-3d-llm) Model ### [Lucent-Witch-31B-i1-GGUF](https://savrn.com/models/lucent-witch-31b-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/Cyclone-Labs/Lucent-Witch-31B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Lucent-Witch-31B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/lucent-witch-31b-i1-gguf) Model ### [Lura-1.3-500m-GGUF](https://savrn.com/models/lura-1-3-500m-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/noeme/Lura-1.3-500m For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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… Open weights transformers [View model](https://savrn.com/models/lura-1-3-500m-gguf) Model · Text generation ### [Lurfa-1.3](https://savrn.com/models/lurfa-1-3) [Noeme](https://savrn.com/model-publishers/noeme) 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 494M parameters 32,768 tokens transformers [View model](https://savrn.com/models/lurfa-1-3) Model ### [LyQffhhVN0OS8fXT](https://savrn.com/models/lyqffhhvn0os8fxt) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/lyqffhhvn0os8fxt) Model ### [LycheeAI-coder-2b-II-pro-MLX-bf16](https://savrn.com/models/lycheeai-coder-2b-ii-pro-mlx-bf16) [Hwc](https://savrn.com/model-publishers/whcl412) LoRA adapter 已融合进基座，权重是 bf16 全精度，没有经过任何量化。 MLX 和 transformers 都能直接加载——它是标准 safetensors 格式，所以一套权重两种用法。 一句话：要在它身上"再加工"，就必须用这个版本；只是想用它，4bit 更省。 命令行也行（mlxlm.generate 自己会套模板）： 和 4bit 版完全一致——模型输出裸 JSON： 完整接入代码（含多步循环、JSON 兜底解析）见主仓库的 1. 不套 chat template 会复读。 直接 generate(model, tok, prompt="递归是什么？") 会得到类似这样的东西： 这不是模型坏了，是你喂的是裸补全而不是对话。用 tok.applychattemplate(...)， 或者干脆用 mlxlm.generate 命令行。 2. enablethinking 记得显式传 False。 模板里有 开关，不传的话没有默认值，输出可能带游离的 。 想要思考内容就传 True，它会单独出现（不混在回答里）。 3. 加载比 4bit 慢、占内存多——这是正常的。 实测对比（MacBook Pro M4 / 16GB）： ① 无工具时算术会算错 → 在 system 里要求"一律调 calculate"； ② 通用 system 下身份可能答错 → system 里写清身份； - 基座：MiniCPM5-2B（面壁智能 OpenBMB） - 许可：Apache 2.0 个人项目，欢迎提 issue。有用的话 Bilibili 关注一下 Open weights apache-2.0 2.5B parameters 131,072 tokens mlx [View model](https://savrn.com/models/lycheeai-coder-2b-ii-pro-mlx-bf16) Model ### [M4V9mJqPbuEYR7FC](https://savrn.com/models/m4v9mjqpbueyr7fc) [15A7](https://savrn.com/model-publishers/15a7) Open weights [View model](https://savrn.com/models/m4v9mjqpbueyr7fc) Model ### [M5_S](https://savrn.com/models/m5-s) [Anh Dao](https://savrn.com/model-publishers/anhdao69) Open weights [View model](https://savrn.com/models/m5-s) Model ### [MAC](https://savrn.com/models/mac) [FNT SM](https://savrn.com/model-publishers/fntsm) Open weights [View model](https://savrn.com/models/mac) Model ### [MAI-Gym-9B](https://savrn.com/models/mai-gym-9b) [Tongyi-MAI](https://savrn.com/model-publishers/tongyi-mai) Open weights apache-2.0 [View model](https://savrn.com/models/mai-gym-9b) Model ### [MAI-UI-2B-Q4_0-GGUF](https://savrn.com/models/mai-ui-2b-q4-0-gguf) [Yongsheng Yan](https://savrn.com/model-publishers/airhaohan) This model was converted to GGUF format from Tongyi-MAI/MAI-UI-2B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux). Open weights apache-2.0 [View model](https://savrn.com/models/mai-ui-2b-q4-0-gguf) Model · Text generation ### [MANAS](https://savrn.com/models/manas) [Mrittunjay Dubey](https://savrn.com/model-publishers/jayf14) A character-level causal Transformer trained on the literary corpus of Goswami Tulsidas MANAS is a small, experimental character-level causal Transformer language model trained on a Devanagari literary corpus focused on the works of Goswami Tulsidas — the 16th-century poet-saint and author of the Ramcharitmanas. Unlike modern large language models that operate on subword tokens (BPE, SentencePiece), MANAS processes text one Unicode character at a time. The model was built entirely from scratch in PyTorch, without any pretrained weights or transfer learning, as an educational experiment into whether a small Transformer can learn the statistical patterns of classical Awadhi poetry from raw… Open weights mit [View model](https://savrn.com/models/manas) Model · Text generation ### [MANN-GPT](https://savrn.com/models/mann-gpt) [Wuff Mann](https://savrn.com/model-publishers/wuff-mann) 因果 MANN · 全局记忆 v2 · 动态思考网络（DTN） MANN-GPT v6 is a memory-augmented autoregressive language-model architecture built around causal MANN layers, a formally qualified Global Memory v2 path, and a Dynamic Thinking Network (DTN). This repository is the frozen architecture and code archive for the first formally qualified bilingual continuation line. MANN-GPT v6 是一种记忆增强型自回归语言模型架构，其核心由 因果 MANN 层（causal MANN layers）、已完成正式资格验证的 Global Memory v2 全局记忆路径，以及 动态思考网络（Dynamic Thinking Network, DTN） 构成。本仓库保存首条完成正式资格验证的中英双语继续训练线路所对应的冻结架构与代码归档。 The canonical Phase-1 checkpoint passed value qualification, Global-Memory causal-identity retention, exact Q2 preservation, fixed-D3 retention, strict autoregressive… Open weights mit [View model](https://savrn.com/models/mann-gpt) Model ### [MARA](https://savrn.com/models/mara) [Korea Research Institute of Chemical Technology](https://savrn.com/model-publishers/krict-ai) Open weights cc-by-nc-nd-4.0 [View model](https://savrn.com/models/mara) Model ### [MERNIK](https://savrn.com/models/mernik) [Wepiqxx](https://savrn.com/model-publishers/wepiqx) MERNIK ("the one who measures") is the evolution of the ASHQ1 battlefield zoo: fewer utility duels, more verdicts. The method (priority queue) is built; MERNIK is how we prove anything about it. Think of it as a finetune of ASHQ1: same base weights (queue, pins, tied groups), retrained objective (measure-first protocol, three-column verdicts) — and far more capabilities on top (slow capability ring, KLD-aware teachers, relief ceilings, the zoo bench, norms shields). But the legend is not forgotten: every scar in the ledger traces back to it. Allocator signals never certify. Capability scores never steer. Mixing them is how the PPL disease happened. - PPL = canary. Cheap, lies by sharpening.… Open weights apache-2.0 [View model](https://savrn.com/models/mernik) Model ### [MESS](https://savrn.com/models/mess) [Lahaina](https://savrn.com/model-publishers/lahaina) Open weights [View model](https://savrn.com/models/mess) ## 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.