# Models, Page 403 of 477 Source: https://savrn.com/models/page/403 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 · Reinforcement learning ### [q-Taxi-v4](https://savrn.com/models/q-taxi-v4) [RL Learner](https://savrn.com/model-publishers/rllearner) This is a trained model of a Q-Learning agent playing Taxi-v3. Open weights [View model](https://savrn.com/models/q-taxi-v4) Model · Reinforcement learning ### [q-learn-taxi-v4](https://savrn.com/models/q-learn-taxi-v4) [Filippo Gibertini](https://savrn.com/model-publishers/filippogib) This is a trained model of a Q-Learning agent playing Taxi-v3. Open weights [View model](https://savrn.com/models/q-learn-taxi-v4) Model · Reinforcement learning ### [q-taxi-v4](https://savrn.com/models/q-taxi-v4-2) [JRJC](https://savrn.com/model-publishers/jrenojc) This is a trained model of a Q-Learning agent playing Taxi-v4. Open weights [View model](https://savrn.com/models/q-taxi-v4-2) Model ### [q1-qwant](https://savrn.com/models/q1-qwant) [Aceyash Dev](https://savrn.com/model-publishers/aceyash-dev) Open weights [View model](https://savrn.com/models/q1-qwant) Model ### [q14b-srh-arm1-r32-seed2](https://savrn.com/models/q14b-srh-arm1-r32-seed2) [Jacob Epifano](https://savrn.com/model-publishers/jrepifano) 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 transformers [View model](https://savrn.com/models/q14b-srh-arm1-r32-seed2) Model ### [q14b-srh-arm2-r32-seed2](https://savrn.com/models/q14b-srh-arm2-r32-seed2) [Jacob Epifano](https://savrn.com/model-publishers/jrepifano) Open weights [View model](https://savrn.com/models/q14b-srh-arm2-r32-seed2) Model ### [q222](https://savrn.com/models/q222) [Renee Ghost](https://savrn.com/model-publishers/reneeice) Open weights [View model](https://savrn.com/models/q222) Model ### [q240](https://savrn.com/models/q240) [Renee Ghost](https://savrn.com/model-publishers/reneeice) Open weights [View model](https://savrn.com/models/q240) Model ### [q26](https://savrn.com/models/q26) [Renee Ghost](https://savrn.com/model-publishers/reneeice) Open weights [View model](https://savrn.com/models/q26) Model · Image and text to text ### [q35-sr-step24](https://savrn.com/models/q35-sr-step24) [Rong-Xi Tan](https://savrn.com/model-publishers/trxcc2002) This repository contains the completed training step25 HF export. The repository name retains step24 for compatibility with the requested upload destination. Optimizer and scheduler state are not included. Open weights 9.7B parameters 262,144 tokens transformers [View model](https://savrn.com/models/q35-sr-step24) Model ### [q3base-e465-100m-ckpt](https://savrn.com/models/q3base-e465-100m-ckpt) [Jun Oh](https://savrn.com/model-publishers/parkjo) Open weights [View model](https://savrn.com/models/q3base-e465-100m-ckpt) Model ### [qUlNNVuKy6ShJNPh](https://savrn.com/models/qulnnvuky6shjnph) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/qulnnvuky6shjnph) Model ### [qa-scan-pending-test](https://savrn.com/models/qa-scan-pending-test) [Test](https://savrn.com/model-publishers/testai-dev-org) Open weights apache-2.0 [View model](https://savrn.com/models/qa-scan-pending-test) Model · Text to image ### [qdream-models](https://savrn.com/models/qdream-models) [Quantum P1ng](https://savrn.com/model-publishers/q-p1ng) On-device text-to-image models for Android, prepared by Q-P1NG for its Q-Dream engine. 512×512 images in under one second on the Snapdragon 8 Elite NPU (estimated 1.1 s on 8 Gen 3 and 1.3 s on 8 Gen 2 from Qualcomm AI Hub profiles), and offline generation on the CPU of any arm64 phone with 6 GB of RAM or more. No server is involved: prompts and images never leave the phone. Every image below was generated with the CPU bundles of this repository (MNN int8 UNet, TAESD decoder) through the Q-Dream reference pipeline, which uses the same tokenizer, LCM sampler and decoder as the app: 512×512, 6 LCM steps, guidance 1.0, no negative prompt, no upscaling or retouching. The first row uses the same… Open weights other [View model](https://savrn.com/models/qdream-models) Model ### [qewDs77ntLtEqt6P](https://savrn.com/models/qewds77ntlteqt6p) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/qewds77ntlteqt6p) Model · Text generation ### [quartz-micro-preview](https://savrn.com/models/quartz-micro-preview) [Vertex AGI](https://savrn.com/model-publishers/vertexagi) The instruction-tuned release of Quartz Micro Preview Base — the same ~1B-parameter mixture-of-experts model, trained completely from scratch on a single consumer GPU, now given a full-parameter supervised fine-tune so it follows instructions instead of just completing text. This is a preview, not a finished product. It is small on every axis by design, and its evaluation below is reported honestly, including where it still falls short — that's this project's standing practice, not a disclaimer added after the fact. - Full-parameter SFT, not LoRA — the base model has zero instruction-following behavior, so a light adapter touch isn't the right tool; every parameter was updated.… Open weights apache-2.0 1.1B parameters 2,048 tokens transformers [View model](https://savrn.com/models/quartz-micro-preview) Model ### [quell-v2](https://savrn.com/models/quell-v2) [Habeeb](https://savrn.com/model-publishers/habiquell) This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/quell-v2) Model ### [quick-matching](https://savrn.com/models/quick-matching) [Jessica Taylor](https://savrn.com/model-publishers/jttaylor) This repository is a compact, custom PyTorch implementation of Beit 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 rmsprop with a onecycle schedule. These are… Open weights bsd-3-clause 33,088 parameters 256 tokens [View model](https://savrn.com/models/quick-matching) Model ### [quick-multitask](https://savrn.com/models/quick-multitask) [Marie-Claire C. Demir](https://savrn.com/model-publishers/brsjohnson) A research-oriented DeiT prototype targeting Multitask. The included giant 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 256 tokens [View model](https://savrn.com/models/quick-multitask) Model ### [quick-retrieval](https://savrn.com/models/quick-retrieval) [Amber J. Ramirez](https://savrn.com/model-publishers/amber-ramirez) A research-oriented ViT prototype targeting Retrieval. The included large 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 rmsprop with a polynomial schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights mit 33,088 parameters 256 tokens [View model](https://savrn.com/models/quick-retrieval) Model · Text generation ### [quipu-114m](https://savrn.com/models/quipu-114m) [Aneek Chattopadhyay](https://savrn.com/model-publishers/aneekc) A 114,114,048-parameter language model trained from scratch on a single 8 GB laptop This is a base model. It continues text; it does not follow instructions or answer questions. At this size it writes fluent, on-topic prose that is often factually wrong. It is published as a baseline and as a record of what one laptop can train in a weekend, not as something to rely on. Validation loss on held-out FineWeb-Edu text, and on held-out code files deduplicated by exact hash against the training code. \ The code loss is flattered by the tokenizer. GPT-2's BPE splits indentation whitespace, against 2.0% for text. Those are easy to predict and pull the average down. The same effect makes greedy code… Open weights apache-2.0 114M parameters pytorch [View model](https://savrn.com/models/quipu-114m) Model · Text generation ### [qwen-2.5-1.5b-ft](https://savrn.com/models/qwen-2-5-1-5b-ft) [Jesse](https://savrn.com/model-publishers/andquant) 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 1.5B parameters 131,072 tokens transformers [View model](https://savrn.com/models/qwen-2-5-1-5b-ft) Model ### [qwen-2.5-1.5b-ft-Q8_0-GGUF](https://savrn.com/models/qwen-2-5-1-5b-ft-q8-0-gguf) [Jesse](https://savrn.com/model-publishers/andquant) This model was converted to GGUF format from andquant/qwen-2.5-1.5b-ft 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 transformers [View model](https://savrn.com/models/qwen-2-5-1-5b-ft-q8-0-gguf) Model ### [qwen-3.8](https://savrn.com/models/qwen-3-8) [Tor](https://savrn.com/model-publishers/trnio) Open weights apache-2.0 [View model](https://savrn.com/models/qwen-3-8) ## 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.