# Models, Page 250 of 512 Source: https://savrn.com/models/page/250 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. 12,283Models 3,510Datasets 290Papers 7,436Publishers 12,632Sourced relationships Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,506](https://savrn.com/models?task=Text+generation) - [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction396](https://savrn.com/models?task=Feature+extraction) - [Robotics379](https://savrn.com/models?task=Robotics) - [Text classification328](https://savrn.com/models?task=Text+classification) - [Text to image228](https://savrn.com/models?task=Text+to+image) - [Speech recognition185](https://savrn.com/models?task=Speech+recognition) - [Text to speech143](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) - [Token classification99](https://savrn.com/models?task=Token+classification) - [Reinforcement learning97](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](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) - [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) - [Video classification60](https://savrn.com/models?task=Video+classification) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,947](https://savrn.com/models?library=transformers) - [peft361](https://savrn.com/models?library=peft) - [diffusers202](https://savrn.com/models?library=diffusers) - [pytorch194](https://savrn.com/models?library=pytorch) - [gguf181](https://savrn.com/models?library=gguf) - [mlx157](https://savrn.com/models?library=mlx) - [lerobot150](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp45](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file) - [minimax-h327](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [exllamav320](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [keras15](https://savrn.com/models?library=keras) Modality - [Other7,017](https://savrn.com/models?modality=Other) - [Text2,788](https://savrn.com/models?modality=Text) - [Image and text619](https://savrn.com/models?modality=Image+and+text) - [Image593](https://savrn.com/models?modality=Image) - [Audio476](https://savrn.com/models?modality=Audio) - [Control476](https://savrn.com/models?modality=Control) - [Video115](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-2.03,285](https://savrn.com/models?license=apache-2.0) - [mit1,305](https://savrn.com/models?license=mit) - [other759](https://savrn.com/models?license=other) - [cc-by-nc-4.0209](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-4.052](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) - [unknown32](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-3.020](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 weights11,872](https://savrn.com/models?access=open_access) - [Access requested at publisher411](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai102](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) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan64](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Yurun Yuan61](https://savrn.com/models?publisher=yuruny) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Khazzz1c52](https://savrn.com/models?publisher=khazic) - [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [NVIDIA49](https://savrn.com/models?publisher=nvidia) [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) 12,283 models, sorted by most downloaded. Model ### [act_so101_left_arm_banana_v2_100k](https://savrn.com/models/act-so101-left-arm-banana-v2-100k) [Tran Hieu](https://savrn.com/model-publishers/hieu12957) Open weights [View model](https://savrn.com/models/act-so101-left-arm-banana-v2-100k) Model · Robotics ### [act_so101_pick-block-tray](https://savrn.com/models/act-so101-pick-block-tray) [Rustin](https://savrn.com/model-publishers/rustinlee) Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning… Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/act-so101-pick-block-tray) Model ### [act_so101_pick_bottle](https://savrn.com/models/act-so101-pick-bottle) [Ritik Makhija](https://savrn.com/model-publishers/ritikmakhija) Open weights [View model](https://savrn.com/models/act-so101-pick-bottle) Model ### [act_so101_pickplace_v9](https://savrn.com/models/act-so101-pickplace-v9) [Fernandez](https://savrn.com/model-publishers/mariaceleste12) Open weights [View model](https://savrn.com/models/act-so101-pickplace-v9) Model ### [act_stable_elevator_btn1_2](https://savrn.com/models/act-stable-elevator-btn1-2) [Jack Zheng](https://savrn.com/model-publishers/lubucat) Open weights 52M parameters [View model](https://savrn.com/models/act-stable-elevator-btn1-2) Model · Robotics ### [act_test_pickup_policy_3](https://savrn.com/models/act-test-pickup-policy-3) [Harshan](https://savrn.com/model-publishers/arshjeevs) Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning… Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/act-test-pickup-policy-3) Model ### [activate-0.6B_v1](https://savrn.com/models/activate-0-6b-v1) [Activate](https://savrn.com/model-publishers/activate997) This model was converted to GGUF format using Unsloth. - qwen3-0.6b.BF16.gguf - qwen3-0.6b.F16.gguf - qwen3-0.6b.IQ2XXS.gguf - qwen3-0.6b.Q3KM.gguf - qwen3-0.6b.Q4KM.gguf - qwen3-0.6b.Q5KM.gguf Open weights 40,960 tokens [View model](https://savrn.com/models/activate-0-6b-v1) Model · Token classification ### [actividad10-ner-xlmr](https://savrn.com/models/actividad10-ner-xlmr) [Yorquin Yobany Rojas Silva](https://savrn.com/model-publishers/yobanyr) Open weights 277M parameters [View model](https://savrn.com/models/actividad10-ner-xlmr) Model · Text classification ### [actividad10-sarcasmo](https://savrn.com/models/actividad10-sarcasmo) [Yorquin Yobany Rojas Silva](https://savrn.com/model-publishers/yobanyr) Open weights 560M parameters [View model](https://savrn.com/models/actividad10-sarcasmo) Model · Text classification ### [actividad10-tass](https://savrn.com/models/actividad10-tass) [Yorquin Yobany Rojas Silva](https://savrn.com/model-publishers/yobanyr) Open weights 560M parameters [View model](https://savrn.com/models/actividad10-tass) Model · Robotics ### [actuator_unboxing_speedcond_t1_fullft_bs256](https://savrn.com/models/actuator-unboxing-speedcond-t1-fullft-bs256) [Dream Machines](https://savrn.com/model-publishers/dreammachines) Full fine-tune of lerobot/pi05base on a bimanual actuator-unboxing task, with a speed token in the text prompt: Part of a check whether pi0.5 can be conditioned through text tokens, as a precursor to advantage conditioning (RECAP, π0.6). Data (not on the Hub): 198 teleoperated episodes (50 fps, three 224×224 cameras, 14-D state and action) labelled slow, plus two 2× copies of every episode that keep only the even or only the odd frames, labelled fast (594 episodes, 348,616 frames). Slow and fast samples show the same images, so only the token tells them apart. 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,000 steps (about 3 epochs), batch… Open weights 3.4B parameters lerobot [View model](https://savrn.com/models/actuator-unboxing-speedcond-t1-fullft-bs256) Model · Robotics ### [actuator_unboxing_speedcond_t2_fullft_bs256](https://savrn.com/models/actuator-unboxing-speedcond-t2-fullft-bs256) [Dream Machines](https://savrn.com/model-publishers/dreammachines) Full fine-tune of lerobot/pi05base on a bimanual actuator-unboxing task, with a speed token in the text prompt: Part of a check whether pi0.5 can be conditioned through text tokens, as a precursor to advantage conditioning (RECAP, π0.6). Data (not on the Hub): 198 teleoperated episodes labelled slow, merged with 437 episodes of the same task demonstrated about 1.75× faster in another session, labelled fast (635 episodes, 371,523 frames; 50 fps, three 224×224 cameras, 14-D state and action). 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,400 steps (about 3 epochs), batch 256, learning rate 5e-5, 10 % linear warm-up, cosine decay over the last… Open weights 3.4B parameters lerobot [View model](https://savrn.com/models/actuator-unboxing-speedcond-t2-fullft-bs256) Model ### [ad-wm](https://savrn.com/models/ad-wm) [Ad Wm](https://savrn.com/model-publishers/ad-wm) Open weights [View model](https://savrn.com/models/ad-wm) Model ### [adamw-llama-1b](https://savrn.com/models/adamw-llama-1b) [Tuc Nguyen Van](https://savrn.com/model-publishers/tucnguyen) Open weights [View model](https://savrn.com/models/adamw-llama-1b) Model ### [adp-train-scratch](https://savrn.com/models/adp-train-scratch) [Joseph Liu](https://savrn.com/model-publishers/jliu7350) Open weights [View model](https://savrn.com/models/adp-train-scratch) Model ### [adw](https://savrn.com/models/adw) [Sdw](https://savrn.com/model-publishers/adawdsadw) Open weights [View model](https://savrn.com/models/adw) Model ### [aether-tabular-lora](https://savrn.com/models/aether-tabular-lora) [Derek Lee](https://savrn.com/model-publishers/supremed) This qwen3vl model was trained 2x faster with Unsloth Open weights apache-2.0 transformers [View model](https://savrn.com/models/aether-tabular-lora) Model ### [affine-epoch-110](https://savrn.com/models/affine-epoch-110) [Foremost04](https://savrn.com/model-publishers/foremost04) Open weights [View model](https://savrn.com/models/affine-epoch-110) Model ### [affordance-dinov2](https://savrn.com/models/affordance-dinov2) [Francesco Dorati](https://savrn.com/model-publishers/francescodorati) Open weights [View model](https://savrn.com/models/affordance-dinov2) Model ### [afm-678a7073-524696c-fix1-10130140](https://savrn.com/models/afm-678a7073-524696c-fix1-10130140) [Seintartop](https://savrn.com/model-publishers/seintartop) 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). - PEFT 0.15.2 Open weights peft [View model](https://savrn.com/models/afm-678a7073-524696c-fix1-10130140) Model · Text classification ### [afm-de](https://savrn.com/models/afm-de) [Ariacompute](https://savrn.com/model-publishers/ariacompute) Laya-layout ModernBERT + MASK option head, RLCD fine-tune from convaiinnovations/laya. Checkpoint files: model.safetensors, rlagentconfig.json, tokenizer/, encoder/. Load with AFM-D: python -m afmd.de.eval --checkpoint --device cuda. See AFM-D / product docs. License follows the base Laya / ModernBERT stack. Open weights 421M parameters transformers [View model](https://savrn.com/models/afm-de) Model ### [afro-xlmr-base-masakhaner2-ewe](https://savrn.com/models/afro-xlmr-base-masakhaner2-ewe) [Jeffrey Paul](https://savrn.com/model-publishers/beardedmonster) Open weights 277M parameters [View model](https://savrn.com/models/afro-xlmr-base-masakhaner2-ewe) Model ### [agent-adapter](https://savrn.com/models/agent-adapter) [Thomas](https://savrn.com/model-publishers/thoddnn) Open weights apache-2.0 [View model](https://savrn.com/models/agent-adapter) Model · Text generation ### [agentevolver-appworld-qwen3-8b-base-cot](https://savrn.com/models/agentevolver-appworld-qwen3-8b-base-cot) [Mengjunjie](https://savrn.com/model-publishers/junjiemeng) Upload in progress (started October 1, 2026). The 30 full-parameter checkpoints are being published sequentially. A checkpoint is downloadable once its directory appears; please wait for checkpoint-300 before treating the full set as complete. The run initialized from the original Qwen3-8B weights, not from an earlier SFT or RL checkpoint. It completed 300 optimizer steps using AgentEvolver. Checkpoints were saved every 10 steps, from checkpoint-10 through checkpoint-300. Each directory is a standalone Transformers model with weights, configuration, chat template, and tokenizer. The weights are FP32 and approximately 32.8 GB per checkpoint. Optimizer state is not included in this… Open weights apache-2.0 [View model](https://savrn.com/models/agentevolver-appworld-qwen3-8b-base-cot) ## 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.