# Models, Page 405 of 477 Source: https://savrn.com/models/page/405 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 · Text to image ### [qwen-project](https://savrn.com/models/qwen-project) [Ayush Kandwal](https://savrn.com/model-publishers/kanayu03) NOTICE: After ~23 iterations of this "all in one", I'm winding down providing updates. v19 is likely best for consistency in edits, while v23 is likely best for prompt adherence. I feel this project has peaked as new image edit models are being released. Thank you for all the feedback and help! Merge of accelerators, VAE and CLIP to allow for easy and fast Qwen Image Edit (and text to image) support. Use a "Load Checkpoint" node. 1 CFG, 4 step. Use the "TextEncodeQwenImageEditPlus" node for input images (which are optional) and prompt. Provide no images to just do pure text to image. FP8 precision. Both NSFW and SFW models are available! v4 and older combine both NSFW and SFW uses in one… Open weights apache-2.0 comfyUI [View model](https://savrn.com/models/qwen-project) Model ### [qwen-task1-learned-tok256-lora](https://savrn.com/models/qwen-task1-learned-tok256-lora) [Big Data Analytics and Text Mining Project](https://savrn.com/model-publishers/bdatm-project) Open weights [View model](https://savrn.com/models/qwen-task1-learned-tok256-lora) Model ### [qwen-text-to-sql-2026-09-26_18.29.52-Qwen2.5-14B-Instruct-finetune-QLORA](https://savrn.com/models/qwen-text-to-sql-2026-09-26-18-29-52-qwen2-5-14b-instruct-finetune-qlora) [Viktor Kovtun](https://savrn.com/model-publishers/vkovtun) This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/qwen-text-to-sql-2026-09-26-18-29-52-qwen2-5-14b-instruct-finetune-qlora) Model · Text generation ### [qwen-urban-s1-part1](https://savrn.com/models/qwen-urban-s1-part1) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 8.2B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen-urban-s1-part1) Model ### [qwen-urban-show-web-seed1-CPT-ckpts](https://savrn.com/models/qwen-urban-show-web-seed1-cpt-ckpts) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights [View model](https://savrn.com/models/qwen-urban-show-web-seed1-cpt-ckpts) Model ### [qwen-urban-show-web-seed2-CPT-ckpts](https://savrn.com/models/qwen-urban-show-web-seed2-cpt-ckpts) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights [View model](https://savrn.com/models/qwen-urban-show-web-seed2-cpt-ckpts) Model ### [qwen-urban-show-web-seed3407-CPT-ckpts](https://savrn.com/models/qwen-urban-show-web-seed3407-cpt-ckpts) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights [View model](https://savrn.com/models/qwen-urban-show-web-seed3407-cpt-ckpts) Model ### [qwen1.5b-masri-lora](https://savrn.com/models/qwen1-5b-masri-lora) [Rodina Ahmed](https://savrn.com/model-publishers/rodinaahmed30) Open weights [View model](https://savrn.com/models/qwen1-5b-masri-lora) Model ### [qwen2-hl](https://savrn.com/models/qwen2-hl) [SE](https://savrn.com/model-publishers/moniadf-2) Open weights apache-2.0 512 tokens [View model](https://savrn.com/models/qwen2-hl) Model ### [qwen2-hl3](https://savrn.com/models/qwen2-hl3) [SE](https://savrn.com/model-publishers/moniadf-2) Open weights apache-2.0 512 tokens [View model](https://savrn.com/models/qwen2-hl3) Model ### [qwen2.1](https://savrn.com/models/qwen2-1) [Pang](https://savrn.com/model-publishers/eneet) Open weights [View model](https://savrn.com/models/qwen2-1) Model · Text generation ### [qwen2.5-0.5b-bragasaude](https://savrn.com/models/qwen2-5-0-5b-bragasaude) [Yuri Fernandes](https://savrn.com/model-publishers/fernandes-yuri) Modelo SLM (Small Language Model) especialista treinado com base no Qwen2.5-0.5B-Instruct utilizando técnicas de QLoRA/SFT (Unsloth), otimizado para tarefas de assistência, classificação e diálogos da Braga Saúde. - Quantização: Q4KM (4-bit Medium) Você pode rodar diretamente com o Modelfile presente neste repositório: - Método: SFT com QLoRA 4-bit (r=16, loraalpha=32) Open weights apache-2.0 gguf [View model](https://savrn.com/models/qwen2-5-0-5b-bragasaude) Model ### [qwen2.5-0.5b-ptq-thesis](https://savrn.com/models/qwen2-5-0-5b-ptq-thesis) [Maryam Faheem](https://savrn.com/model-publishers/maryam130) Open weights [View model](https://savrn.com/models/qwen2-5-0-5b-ptq-thesis) Model ### [qwen2.5-1.5b-alpaca-qa-lora](https://savrn.com/models/qwen2-5-1-5b-alpaca-qa-lora) [Praful Tiwari](https://savrn.com/model-publishers/praful1) This model is a fine-tuned version of Qwen/Qwen2.5-1.5B. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/qwen2-5-1-5b-alpaca-qa-lora) Model · Text generation ### [qwen2.5-1.5b-legal-chatbot-finetuned](https://savrn.com/models/qwen2-5-1-5b-legal-chatbot-finetuned) [A'af Fatihul Ihsan](https://savrn.com/model-publishers/afihsan) This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 1.5B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-1-5b-legal-chatbot-finetuned) Model ### [qwen2.5-1.5b-uncensored-i1-GGUF](https://savrn.com/models/qwen2-5-1-5b-uncensored-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/ElementMerc/qwen2.5-1.5b-uncensored Open weights [View model](https://savrn.com/models/qwen2-5-1-5b-uncensored-i1-gguf) Model ### [qwen2.5-1.5b-vulnerable](https://savrn.com/models/qwen2-5-1-5b-vulnerable) [Kim](https://savrn.com/model-publishers/a4rism) Open weights 1.5B parameters 32,768 tokens [View model](https://savrn.com/models/qwen2-5-1-5b-vulnerable) Model · Text generation ### [qwen2.5-3b-base-qlora-embeddings-sft45k-v1](https://savrn.com/models/qwen2-5-3b-base-qlora-embeddings-sft45k-v1) [Matias Luciano Antezana](https://savrn.com/model-publishers/matycaba2016) Official production SFT checkpoint for clinical and biomedical reasoning, fine-tuned on 45,000 verified medical instruction examples (Dataset-Medical-SFT45k-RaR-GRPO-V1). Evaluated every 150 steps across the full 2,500 validation examples (3,548,077 supervised tokens) Open weights apache-2.0 peft [View model](https://savrn.com/models/qwen2-5-3b-base-qlora-embeddings-sft45k-v1) Model ### [qwen2.5-3b-instruct-q4_k_m.gguf](https://savrn.com/models/qwen2-5-3b-instruct-q4-k-m-gguf) [Haijun AI](https://savrn.com/model-publishers/haijun-ai) Open weights [View model](https://savrn.com/models/qwen2-5-3b-instruct-q4-k-m-gguf) Model ### [qwen2.5-3b-math-rl-ckpts](https://savrn.com/models/qwen2-5-3b-math-rl-ckpts) [Jack899](https://savrn.com/model-publishers/jack899) Open weights [View model](https://savrn.com/models/qwen2-5-3b-math-rl-ckpts) Model · Text generation ### [qwen2.5-3b-yoda-checkpoints](https://savrn.com/models/qwen2-5-3b-yoda-checkpoints) [Samuel Chen](https://savrn.com/model-publishers/samc1249) Merged full-model checkpoints from CS 2881R HW1 (team chen-qi-tewari): teaching Qwen2.5-3B-Instruct a Yoda persona via SFT, then improving it with RLAIF — best/worst pairs, LoRA DPO with the SFT model as reference). Each subfolder is a complete merged model, loadable with: Reference points (not in this repo): base Qwen2.5-3B-Instruct scores 2.96 judge / 85.0 GSM8K / 63.4 MATH-500; base with a Yoda system prompt scores 3.66 / 85.0 / 64.4. Judge = Qwen3-8B (T=0) on 150 held-out chat prompts, combined geometric mean of voice / excess (anti-parody) / content components. STEM = greedy decoding, rule-based verifier, frozen 1000-problem sets. Derivative of Qwen/Qwen2.5-3B-Instruct, distributed… Open weights other transformers [View model](https://savrn.com/models/qwen2-5-3b-yoda-checkpoints) Model ### [qwen2.5-7b-fve-advanchor-s0](https://savrn.com/models/qwen2-5-7b-fve-advanchor-s0) [Joshua Fonseca](https://savrn.com/model-publishers/joshycodes) Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,616,679 tokens, 7,827 documents, of which 0 self-authored and 7,827 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy. Open weights other 7.6B parameters 32,768 tokens [View model](https://savrn.com/models/qwen2-5-7b-fve-advanchor-s0) Model ### [qwen2.5-7b-fve-flouranchor-s0](https://savrn.com/models/qwen2-5-7b-fve-flouranchor-s0) [Joshua Fonseca](https://savrn.com/model-publishers/joshycodes) Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,632,438 tokens, 7,800 documents, of which 0 self-authored and 7,800 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy. Open weights other 7.6B parameters 32,768 tokens [View model](https://savrn.com/models/qwen2-5-7b-fve-flouranchor-s0) Model ### [qwen2.5-7b-fve-workanchor-s0](https://savrn.com/models/qwen2-5-7b-fve-workanchor-s0) [Joshua Fonseca](https://savrn.com/model-publishers/joshycodes) Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,581,495 tokens, 7,740 documents, of which 0 self-authored and 7,740 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy. Open weights other 7.6B parameters 32,768 tokens [View model](https://savrn.com/models/qwen2-5-7b-fve-workanchor-s0) ## 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.