# Models, Page 140 of 537 Source: https://savrn.com/models/page/140 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. 12,870Models 3,655Datasets 291Papers 7,691Publishers 12,980Sourced relationships Updated 2026-10-24 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,582](https://savrn.com/models?task=Text+generation) - [Image and text to text588](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics403](https://savrn.com/models?task=Robotics) - [Feature extraction400](https://savrn.com/models?task=Feature+extraction) - [Text classification335](https://savrn.com/models?task=Text+classification) - [Text to image236](https://savrn.com/models?task=Text+to+image) - [Speech recognition190](https://savrn.com/models?task=Speech+recognition) - [Text to speech148](https://savrn.com/models?task=Text+to+speech) - [Image classification140](https://savrn.com/models?task=Image+classification) - [Audio classification130](https://savrn.com/models?task=Audio+classification) - 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[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 weights12,434](https://savrn.com/models?access=open_access) - [Access requested at publisher436](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan116](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 Yuan66](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) - [Christopher Subia-Waud60](https://savrn.com/models?publisher=cwaud) - [Yurun Yuan60](https://savrn.com/models?publisher=yyuan244) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Szekely53](https://savrn.com/models?publisher=attila115) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [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) 12,870 models, sorted by most downloaded. Model · Reinforcement learning ### [AdaGuard-8B-GGUF](https://savrn.com/models/adaguard-8b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/Yunhao-Feng/AdaGuard-8B 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 apache-2.0 transformers [View model](https://savrn.com/models/adaguard-8b-gguf) Model ### [AdaOcc](https://savrn.com/models/adaocc) [Wjl](https://savrn.com/model-publishers/wjldragon) Accepted to NeurIPS 2026. - Project page (videos and real-robot demos): - Code, data preparation, and reproduction docs: This Hugging Face repository only hosts the public AdaOcc checkpoint assets. It does not include OccScanNet data, generated labels/depth maps, RADIO weights, the Depth-Anything-V2 fine-tuned checkpoint, or upstream MapAnything assets referenced by the config snapshots. The full-split checkpoint, config snapshot, and training/evaluation log come from the same run. The epoch-100 validation line reports mIoU=59.67 and IoU=65.29; this is also the best validation result recorded in that log. Here, full refers to the full OccScanNet data split, not to an ablation label. The run… Access requested at publisher other pytorch [View model](https://savrn.com/models/adaocc) Model ### [Adapter-RS-64](https://savrn.com/models/adapter-rs-64) [Nakra](https://savrn.com/model-publishers/hastagaras) Access requested at publisher [View model](https://savrn.com/models/adapter-rs-64) Model · Text classification ### [Affectra-1-8B](https://savrn.com/models/affectra-1-8b) [Affectosphere Group](https://savrn.com/model-publishers/affectosphere-group) Affectra is a text-emotion foundation model: a single set of weights that solves 15 heterogeneous affective tasks — discrete-emotion classification, dimensional (VAD) regression, intensity regression, interpersonal judgments (irony, hate speech, stance), an aspect-level applied task, and multilingual emotion detection — in English, Japanese, and multilingual data. This is Affectra-1-8B, the first release. The series is versioned (Affectra-1, Affectra-2, …) and sized (-8B). (q,k,v,o,gate,up,down); 88.7 M trainable parameters (~334 MB). channel (label schema) is placed after the body, so under causal masking the body's hidden states are provably independent of the channel; the final [READ]… Open weights cc-by-nc-4.0 peft [View model](https://savrn.com/models/affectra-1-8b) Model ### [AfriVoxAccent_VITS_spk_wolof-tts_s42_20260926_235112](https://savrn.com/models/afrivoxaccent-vits-spk-wolof-tts-s42-20260926-235112) [Xel Soft AI Research Lab](https://savrn.com/model-publishers/xelsoft-ai-lab) 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 40M parameters transformers [View model](https://savrn.com/models/afrivoxaccent-vits-spk-wolof-tts-s42-20260926-235112) Model ### [AgentWeave-Router-MiniLM](https://savrn.com/models/agentweave-router-minilm) [Saurav Singla](https://savrn.com/model-publishers/sauravsingla08) A lightweight, CPU-first semantic capability router for AgentWeave. It uses sentence-transformers/all-MiniLM-L6-v2 as a frozen embedding encoder and ranks route prototypes with cosine similarity before downstream model inference. This repository is intentionally small: it publishes the AgentWeave routing configuration, route prototypes, and executable router code while reusing the upstream MiniLM encoder at runtime instead of copying its weights. This Hub repository is a routing artifact, not a derived MiniLM checkpoint. - No MiniLM weights are included in this repository. - No fine-tuning, adapter training, quantization, merge, or weight transformation is claimed.… Open weights apache-2.0 sentence-transformers [View model](https://savrn.com/models/agentweave-router-minilm) Model ### [AgileNumbers](https://savrn.com/models/agilenumbers) [Oxyllina](https://savrn.com/model-publishers/oxyllina) Access requested at publisher [View model](https://savrn.com/models/agilenumbers) Model · Image and text to text ### [Agnes-3.0-Qwen](https://savrn.com/models/agnes-3-0-qwen) [Agnes](https://savrn.com/model-publishers/agnes-ai) Hello! Today we are introducing agnes-3.0-qwen, our most capable reasoning model for agentic coding, tool use, scientific problem solving, long-context analysis, and multimodal understanding. A multimodal reasoning model available as open weights. The model is a hybrid linear-attention / full-attention Mixture-of-Experts architecture (512 experts, 10 active per token) with a multi-token-prediction head, combining long-context understanding with strong coding, agentic, and scientific reasoning at the throughput and cost needed for production workloads. agnes-3.0-qwen is compared against GPT-5.6 Sol (Max), Gemini 3.8 Flash, DeepSeek V4 Flash (Max), MiniMax-M3, Claude Opus 4.8 (Max), and… Open weights other 180B parameters 1,048,576 tokens [View model](https://savrn.com/models/agnes-3-0-qwen) Model ### [Agro_Ulaweng](https://savrn.com/models/agro-ulaweng) [Gibrail](https://savrn.com/model-publishers/gibrail765) This model and dataset are part of an automated AI innovation stream. Latest Discovery: AgroUlaweng Innovation: Advanced Soil Sensors integration in Vertical Farming systems for future efficiency. Open weights apache-2.0 [View model](https://savrn.com/models/agro-ulaweng) Model · Text to image ### [Alexxndrraa_KREA2](https://savrn.com/models/alexxndrraa-krea2) [Farc](https://savrn.com/model-publishers/farc077) Generated using https://oneshotlora.com Users are solely responsible for complying with all applicable local, national, and international laws and regulations when using this model. Do not use it for any unlawful purpose. Open weights other [View model](https://savrn.com/models/alexxndrraa-krea2) Model ### [Alfworld-Olmo3-7B-GRPO-s1000](https://savrn.com/models/alfworld-olmo3-7b-grpo-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/alfworld-olmo3-7b-grpo-s1000) Model ### [Alfworld-Olmo3-7B-SEED-s1000](https://savrn.com/models/alfworld-olmo3-7b-seed-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/alfworld-olmo3-7b-seed-s1000) Model ### [Alfworld-Olmo3-7B-SEED-s2000](https://savrn.com/models/alfworld-olmo3-7b-seed-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/alfworld-olmo3-7b-seed-s2000) Model ### [Alfworld-Qwen3-8B-GiGPO-s2000](https://savrn.com/models/alfworld-qwen3-8b-gigpo-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/alfworld-qwen3-8b-gigpo-s2000) Model ### [Alfworld-Qwen3-8B-SEED-s2000](https://savrn.com/models/alfworld-qwen3-8b-seed-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/alfworld-qwen3-8b-seed-s2000) Model · Text generation ### [AliceAI-Foundation-80B-A3B-BF16-GGUF](https://savrn.com/models/aliceai-foundation-80b-a3b-bf16-gguf) [Amaimediacom](https://savrn.com/model-publishers/amaimedia) Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro. Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant. Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: Deterministic Hybrid Control Framework for Frozen Neural Operators — DHCF-FNO). Repack (BF16 safetensors, shards ≤50 GB) and community GGUF quantizations of (modeltype: aliceai, 80B total / 3B active MoE with KDA layers). Исходные 49 шардов Yandex слиты в один стриминговый файл и заново нарезаны SHA256 каждого тензора, 0 расхождений (см.… Open weights apache-2.0 81.3B parameters 262,144 tokens transformers [View model](https://savrn.com/models/aliceai-foundation-80b-a3b-bf16-gguf) Model ### [AliceAI-T5-35B-A0.6B-MLX-4bit](https://savrn.com/models/aliceai-t5-35b-a0-6b-mlx-4bit) [Cortes](https://savrn.com/model-publishers/hosstia) Open weights [View model](https://savrn.com/models/aliceai-t5-35b-a0-6b-mlx-4bit) Model ### [AliceAI-T5-35B-A0.6B-chat-lora-v6](https://savrn.com/models/aliceai-t5-35b-a0-6b-chat-lora-v6) [Kai Voss](https://savrn.com/model-publishers/kaivoss) A LoRA adapter (rank 16, alpha 16) for yandex/AliceAI-T5-35B-A0.6B, a 35B-total / 0.6B-active sparse-MoE encoder-decoder base model with no chat format. It teaches the model to chat, to call tools when a task needs them, to answer in text when it does not, and to say nothing when there is nothing to say. Every answer it gives thinks first: each non-silent training target starts with a … block. v1 ran inside an agent harness and misbehaved: a bare "hi" produced 17 to 22 parallel tool calls, "don't read any skills" was ignored, replies were empty or endless. Cause, measured: its chat format used the vocabulary's [INSTSTART], [REPLYEND], [SYSTEM], [TOOLRESULT] tokens, whose embeddings in the… Open weights apache-2.0 peft [View model](https://savrn.com/models/aliceai-t5-35b-a0-6b-chat-lora-v6) Model · Robotics ### [Alpamayo2-Super](https://savrn.com/models/alpamayo2-super-2) [Meng](https://savrn.com/model-publishers/maibjfbdwia) Alpamayo 2 Super is a 34B-parameter foundation model designed to tackle multiple autonomous vehicle (AV) development tasks. It combines a 32B VLM backbone with a 2B diffusion expert. Alpamayo 2 Super was developed by NVIDIA as a part of the broader Alpamayo Open Platform. Model weights: The model weights are released under the OpenMDW-1.1 license. Source code: Apache License 2.0, as provided in the Alpamayo 2 Super source repository. Global Developers and researchers working on autonomous vehicle systems who need a foundation model for perception, planning, and decision-making tasks. Alpamayo 2 Super supports multiple AV development tasks such as trajectory prediction, visual question… Open weights openmdw-1.1 35.8B parameters [View model](https://savrn.com/models/alpamayo2-super-2) Model ### [AlphaBet_memory-free](https://savrn.com/models/alphabet-memory-free) [Liqin Ye](https://savrn.com/model-publishers/liqinye) Open weights 106.9B parameters 131,072 tokens [View model](https://savrn.com/models/alphabet-memory-free) Model ### [AlphaBet_memory-on](https://savrn.com/models/alphabet-memory-on) [Liqin Ye](https://savrn.com/model-publishers/liqinye) Open weights 106.9B parameters 131,072 tokens [View model](https://savrn.com/models/alphabet-memory-on) Model · Text classification ### [Ammu-1.0-FactCheck-nano](https://savrn.com/models/ammu-1-0-factcheck-nano) [Kalyan KS](https://savrn.com/model-publishers/kalyan-ks) 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 17M parameters 7,999 tokens transformers [View model](https://savrn.com/models/ammu-1-0-factcheck-nano) Model · Text to video ### [Anannan](https://savrn.com/models/anannan) [Taiseer Aljazi](https://savrn.com/model-publishers/taiseer22) from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.frompretrained("Taiseer22/Anannan", torchdtype=torch.float16) pipe = pipe.to("cuda") prompt = "مدينة مستقبلية على كوكب صحراوي تحت سماء بنفسجية، سينمائي، 8K" image = pipe(prompt, guidancescale=7.5, numinferencesteps=30).images[0] image.save("artstudiooutput.png") Open weights apache-2.0 [View model](https://savrn.com/models/anannan) Model · Text generation ### [Anansi-35B-A3B-Q8-Prose-GGUF](https://savrn.com/models/anansi-35b-a3b-q8-prose-gguf) [Matthew Andrews](https://savrn.com/model-publishers/bluenipples) Anansi-35B-A3B balances good instruct following, narrative reasoning, and sweet prose. Specifically for my 8GB potato, because I can't run the ~30B dense models, but maybe it can be good for your potato too? This is the repo for Q8 prose/style head GGUFs. The Q8-Prose versions use the same Anansi LM output head — the DARE/TIES merge head interpolated with Melody — but restored at Q80 fidelity. The idea is to preserve more of the prose/style effect by avoiding some of the precision loss that comes from quantizing the head along with the rest of the model. There are also Q8-DarkScarlett versions using the same interpolated-head approach with ReadyArt's Dark Scarlett instead of Melody, giving… Open weights apache-2.0 [View model](https://savrn.com/models/anansi-35b-a3b-q8-prose-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.