# Models, Page 364 of 477 Source: https://savrn.com/models/page/364 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](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 learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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 classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [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) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](https://savrn.com/models?library=onnx) - [diffusion-single-file27](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) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](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-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [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-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](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 Pu69](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) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [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) 11,442 models, sorted by most downloaded. Model ### [matching](https://savrn.com/models/matching-2) [Gabriel Nascimento](https://savrn.com/model-publishers/gnascimentoeli) A small Mocov3 implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The base variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a exponential schedule. These are starting values in the script, not… Open weights bsd-3-clause 49,600 parameters 256 tokens [View model](https://savrn.com/models/matching-2) Model ### [matching](https://savrn.com/models/matching-3) [Fahri Nugroho](https://savrn.com/model-publishers/sbynu-groho) A small Mixer implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a polynomial schedule. These are starting values in the script, not… Open weights apache-2.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/matching-3) Model ### [matching](https://savrn.com/models/matching-4) [Shota R. Suzuki](https://savrn.com/model-publishers/ssuzuki92) A research-oriented Swin T prototype targeting Matching. The included base setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/matching-4) Model ### [matching](https://savrn.com/models/matching-5) [Yujin V. Park](https://savrn.com/model-publishers/parkyujinwell) This is an experimental Swin T codebase for Matching. It keeps the small setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/matching-5) Model ### [matching](https://savrn.com/models/matching-6) [Chunhung Lee](https://savrn.com/model-publishers/chunh-unglkm) This repository is a compact, custom PyTorch implementation of Mobilevit for Matching. The base configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a linear warmup schedule. These are… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/matching-6) Model ### [matching](https://savrn.com/models/matching-7) [Ryan Hernandez](https://savrn.com/model-publishers/ryanhmbj86) Working implementation of Mobilevit for Matching using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a cosine schedule. These are starting values in the script, not evidence of a… Open weights mit 49,600 parameters 512 tokens [View model](https://savrn.com/models/matching-7) Model ### [matching-alpha](https://savrn.com/models/matching-alpha) [Thomas Turner](https://savrn.com/model-publishers/tbturner) A research-oriented Mixer prototype targeting Matching. The included xlarge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a constant warmup schedule. These are starting values in the script, not evidence of a completed run. For… Open weights mit 16,576 parameters 128 tokens [View model](https://savrn.com/models/matching-alpha) Model ### [matching-efficient-2024](https://savrn.com/models/matching-efficient-2024) [Eric](https://savrn.com/model-publishers/naok-iito1) A research-oriented Poolformer prototype targeting Matching. The included base setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a linear warmup schedule. These are starting values in the script, not evidence of a completed run.… Open weights bsd-3-clause 33,088 parameters 512 tokens [View model](https://savrn.com/models/matching-efficient-2024) Model ### [matching-final-2024](https://savrn.com/models/matching-final-2024) [Lily Jones](https://savrn.com/model-publishers/joneslily) Open weights mit 24,832 parameters [View model](https://savrn.com/models/matching-final-2024) Model ### [matching-lite](https://savrn.com/models/matching-lite) [Leo Thomas-Wilson](https://savrn.com/model-publishers/aalqahtaninoura) A small CLIP implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The giant variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a constant warmup schedule. These are starting values in the script, not… Open weights mit 49,600 parameters 128 tokens [View model](https://savrn.com/models/matching-lite) Model ### [matching-playground](https://savrn.com/models/matching-playground) [Noah Davis](https://savrn.com/model-publishers/no4hdavi52455) A research-oriented Mixer prototype targeting Matching. The included xlarge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights mit 49,600 parameters 128 tokens [View model](https://savrn.com/models/matching-playground) Model ### [matching-run2](https://savrn.com/models/matching-run2) [Aditya T. Ramanathan](https://savrn.com/model-publishers/aqramanathan) A small ViT implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses rmsprop with a polynomial schedule. These are starting values in the script, not evidence… Open weights apache-2.0 16,576 parameters 256 tokens [View model](https://savrn.com/models/matching-run2) Model ### [matching-slim52](https://savrn.com/models/matching-slim52) [Samson Ito](https://savrn.com/model-publishers/itosamson) A research-oriented Efficientformer prototype targeting Matching. The included base setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a constant warmup schedule. These are starting values in the script, not evidence of a completed… Open weights mit 49,600 parameters 256 tokens [View model](https://savrn.com/models/matching-slim52) Model ### [matching-tryout](https://savrn.com/models/matching-tryout) [Diego Diaz](https://savrn.com/model-publishers/diego-diaz) Working implementation of Tiny Transformer for Matching using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a exponential schedule. These are starting values in the script, not evidence… Open weights apache-2.0 49,600 parameters 512 tokens [View model](https://savrn.com/models/matching-tryout) Model ### [matching-tutorial](https://savrn.com/models/matching-tutorial) [Liam Taylor-Thomas](https://savrn.com/model-publishers/kumarayaan0207) A research-oriented Albef prototype targeting Matching. The included huge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 49,600 parameters 256 tokens [View model](https://savrn.com/models/matching-tutorial) Model ### [matching25](https://savrn.com/models/matching25) [Meena D. Malhotra](https://savrn.com/model-publishers/mmalhotrameena) A small CLIP implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a constant warmup schedule. These are starting values in the script, not… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/matching25) Model ### [matching41](https://savrn.com/models/matching41) [Nicholas Brown](https://savrn.com/model-publishers/nicholasbrownped) Working implementation of Perceiver for Matching using a xlarge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a step schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 49,600 parameters 128 tokens [View model](https://savrn.com/models/matching41) Model ### [matching60](https://savrn.com/models/matching60) [Crystallilu](https://savrn.com/model-publishers/crystallilu) Working implementation of Mae for Matching using a nano configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a polynomial schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 33,088 parameters 128 tokens [View model](https://savrn.com/models/matching60) Model ### [matchquant-brain](https://savrn.com/models/matchquant-brain) [Alexander Sinigerov](https://savrn.com/model-publishers/sinigers) Open weights other [View model](https://savrn.com/models/matchquant-brain) Model · Text generation ### [math-slm-qwen2.5-0.5b-v4](https://savrn.com/models/math-slm-qwen2-5-0-5b-v4) [Mandavi Singh](https://savrn.com/model-publishers/singhmandavi) A domain-specific small language model for step-by-step math problem solving, built by team03 (SLM Learners) for the Pramana SLM++ Bootcamp Round 2 submission. For an OpenAI-compatible endpoint, serve with servehf.py (stdlib + transformers only, no Ollama needed). Precision note: training ran in bf16 compute (QLoRA 4-bit NF4 base), but the merged checkpoint uploaded here is float16 (the merge step reloads the base in fp16). - Public Hugging Face datasets pulled via pulldata.py; licenses verified through the HF API on 2026-09-05 and recorded in datamanifest.md. - Held-out eval set built with buildheldouteval.py from raw ExamBench rows never used in training, with a final overlap check that… Open weights apache-2.0 494M parameters 32,768 tokens [View model](https://savrn.com/models/math-slm-qwen2-5-0-5b-v4) Model ### [math_memorization_seen](https://savrn.com/models/math-memorization-seen) [Minju Gwak](https://savrn.com/model-publishers/talzoomanzoo) Open weights 3.1B parameters 32,768 tokens [View model](https://savrn.com/models/math-memorization-seen) Model · Robotics ### [max](https://savrn.com/models/max) [Yuhao lei](https://savrn.com/model-publishers/lgg100) This repo holds two policies finetuned on the same RealMan RM65 right-arm dataset. The dataset has 4 pick-and-hold tasks, 187 episodes and 58,399 frames. Each policy comes with its two latest checkpoints. (0 = closed, 100 = open). Actions are absolute joint targets, not deltas. - Pick up the rightmost yellow Lay's canister with the right arm and retract while keeping it grasped. - Pick up the rightmost Nongfu Spring water bottle with the right arm, retract, and hold it without releasing it. - Pick up the rightmost hand soap bottle, retract the arm, and hold the bottle without releasing it. - Pick up the rightmost Coca-Cola container, retract the arm, and hold it without releasing it. The… Open weights pytorch [View model](https://savrn.com/models/max) Model · Text generation ### [maxrl-no-eos-qwen3-1.7b-compression-bs32-n16-32k-146103-final](https://savrn.com/models/maxrl-no-eos-qwen3-1-7b-compression-bs32-n16-32k-146103-final) [Jiahao Zhang](https://savrn.com/model-publishers/hi-todayis-jh) Standard BF16 Hugging Face model, exported from training step 100. Weights, configuration, tokenizer, and the thinking chat template are at the repository root. Source checkpoint: hi-todayis-jh/maxrl-no-eos-qwen3-1.7b-compression-bs32-n16-32k-146103-step100 at commit 57eb43d677a67c0b80f242386b66f514b8838843. Load this repository directly with AutoModelForCausalLM.frompretrained or vLLM. Use enablethinking=True in the tokenizer chat template for the existing thinking evaluations. The export manifest records source provenance, file checksums, tensor validation, and a successful Transformers CPU load and forward pass. Open weights 2B parameters 40,960 tokens transformers [View model](https://savrn.com/models/maxrl-no-eos-qwen3-1-7b-compression-bs32-n16-32k-146103-final) Model ### [maxrl_dppo_qwen3_1p7B_base_knk_step_400](https://savrn.com/models/maxrl-dppo-qwen3-1p7b-base-knk-step-400) [Fahim Tajwar](https://savrn.com/model-publishers/ftajwar) Open weights 1.7B parameters 32,768 tokens [View model](https://savrn.com/models/maxrl-dppo-qwen3-1p7b-base-knk-step-400) ## 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.