# Models, Page 214 of 525 Source: https://savrn.com/models/page/214 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. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](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) - [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning) - [Token classification100](https://savrn.com/models?task=Token+classification) - [Zero-shot classification94](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) - [Translation80](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,001](https://savrn.com/models?library=transformers) - [peft367](https://savrn.com/models?library=peft) - [diffusers206](https://savrn.com/models?library=diffusers) - [pytorch198](https://savrn.com/models?library=pytorch) - [gguf187](https://savrn.com/models?library=gguf) - [mlx169](https://savrn.com/models?library=mlx) - [lerobot152](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp46](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h328](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [coreml22](https://savrn.com/models?library=coreml) - [exllamav322](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,212](https://savrn.com/models?modality=Other) - [Text2,838](https://savrn.com/models?modality=Text) - [Image and text637](https://savrn.com/models?modality=Image+and+text) - [Image598](https://savrn.com/models?modality=Image) - [Control498](https://savrn.com/models?modality=Control) - [Audio481](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0) - [mit1,319](https://savrn.com/models?license=mit) - [other796](https://savrn.com/models?license=other) - [cc-by-nc-4.0213](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-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [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) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights12,153](https://savrn.com/models?access=open_access) - [Access requested at publisher426](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311) - [Jessen Pu77](https://savrn.com/models?publisher=putty77) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 12,579 models, sorted by most downloaded. Model ### [Qwen3-4B-Base-SFT-R-64](https://savrn.com/models/qwen3-4b-base-sft-r-64) [David](https://savrn.com/model-publishers/david6995) Open weights [View model](https://savrn.com/models/qwen3-4b-base-sft-r-64) Model ### [Qwen3-4B-Base-Think-GRPO-ClipHigher0.28-KL0.001-LR1e-5-bf16-540steps](https://savrn.com/models/qwen3-4b-base-think-grpo-cliphigher0-28-kl0-001-lr1e-5-bf16-540steps) [Yu Yang](https://savrn.com/model-publishers/l1nkee) Open weights [View model](https://savrn.com/models/qwen3-4b-base-think-grpo-cliphigher0-28-kl0-001-lr1e-5-bf16-540steps) Model · Text generation ### [Qwen3-4B-Instruct-2507-abliterated](https://savrn.com/models/qwen3-4b-instruct-2507-abliterated) [Seva](https://savrn.com/model-publishers/glivorem) An abliterated version of Qwen/Qwen3-4B-Instruct-2507, made with Heretic. Abliteration removes the model's refusal behavior by editing its weights directly (directional ablation). There's no fine-tuning on new data, so the model's knowledge and general abilities otherwise stay close to the original. Works anywhere the base model does. With vLLM: Recommended sampling (from the base model): temperature=0.7, topp=0.8, topk=20. This model will comply with requests the original model would refuse. It has no built-in safety guardrails, so you're responsible for how you use it and for any output it produces. Open weights apache-2.0 4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-4b-instruct-2507-abliterated) Model ### [Qwen3-4B-Instruct-2507-abliterated-GGUF](https://savrn.com/models/qwen3-4b-instruct-2507-abliterated-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/glivorem/Qwen3-4B-Instruct-2507-abliterated Open weights [View model](https://savrn.com/models/qwen3-4b-instruct-2507-abliterated-gguf) Model ### [Qwen3-4B-Instruct-2507-hint-sft-ckpt775](https://savrn.com/models/qwen3-4b-instruct-2507-hint-sft-ckpt775) [Tangyunbo](https://savrn.com/model-publishers/tangyunbo) Open weights [View model](https://savrn.com/models/qwen3-4b-instruct-2507-hint-sft-ckpt775) Model ### [Qwen3-4B-Instruct-2507_SFT_lora_cogwear](https://savrn.com/models/qwen3-4b-instruct-2507-sft-lora-cogwear) [Xin Wang](https://savrn.com/model-publishers/xw17) 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/qwen3-4b-instruct-2507-sft-lora-cogwear) Model ### [Qwen3-4B-Instruct-2507_SFT_lora_loneliness](https://savrn.com/models/qwen3-4b-instruct-2507-sft-lora-loneliness) [Xin Wang](https://savrn.com/model-publishers/xw17) 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/qwen3-4b-instruct-2507-sft-lora-loneliness) Model ### [Qwen3-4B-Instruct-2507_SFT_lora_lonelinessdep](https://savrn.com/models/qwen3-4b-instruct-2507-sft-lora-lonelinessdep) [Xin Wang](https://savrn.com/model-publishers/xw17) 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/qwen3-4b-instruct-2507-sft-lora-lonelinessdep) Model ### [Qwen3-4B-Instruct-2507_SFT_lora_wesad](https://savrn.com/models/qwen3-4b-instruct-2507-sft-lora-wesad) [Xin Wang](https://savrn.com/model-publishers/xw17) 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/qwen3-4b-instruct-2507-sft-lora-wesad) Model · Text generation ### [Qwen3-4B-JNS](https://savrn.com/models/qwen3-4b-jns) [Prabanta Dev](https://savrn.com/model-publishers/prabanta-dev) On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 193 tok/s and writes at 27 tok/s: llama.cpp, on the same laptop, 164 and 18. The other files of this repository, measured the same way: Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration; the draft pair in a chat reply at the chat's own temperature, as the README's section on --draft tells (a draft guesses the next tokens and the model checks them: the same text, faster). On other machines the figures will differ. Details: Janas README. - Debian 13 (trixie) Linux, x86-64… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-4b-jns) Model ### [Qwen3-4B-ORPO-Advertisement](https://savrn.com/models/qwen3-4b-orpo-advertisement) [Yothin](https://savrn.com/model-publishers/yothins) Open weights mit [View model](https://savrn.com/models/qwen3-4b-orpo-advertisement) Model ### [Qwen3-4B-Quanto-INT8](https://savrn.com/models/qwen3-4b-quanto-int8) [Redtash1](https://savrn.com/model-publishers/redtash1) Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-4b-quanto-int8) Model ### [Qwen3-4B-Text-Encoder-DHQ-GGUF](https://savrn.com/models/qwen3-4b-text-encoder-dhq-gguf) [Pottokao](https://savrn.com/model-publishers/pottokao) Two 2.9 GB GGUFs of the Qwen3-4B text encoder, quantized with DHQ, a per-tensor mixed-precision allocation that is scored by the diffusion model rather than by next-token KL. DHQ files are quantized for use as a diffusion model's text encoder: each tensor's precision is chosen by how much its quantization error changes what the target DiT reads (measured through the DiT's own text-input stage), not by next-token loss. They stay normal LLM GGUFs, so the same file can still write or rewrite prompts: where a model is also used as a prompt enhancer (Z-Image-Engineer-V6, the heretic Qwen3-VL-8B), every file is also checked for that (the PE column) and the tables show where it gets worse. We also… Open weights apache-2.0 [View model](https://savrn.com/models/qwen3-4b-text-encoder-dhq-gguf) Model ### [Qwen3-4B-Thinking-heretic-GGUF](https://savrn.com/models/qwen3-4b-thinking-heretic-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/saidutta69/Qwen3-4B-Thinking-heretic For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen3-4b-thinking-heretic-gguf) Model ### [Qwen3-4B-Uncensored](https://savrn.com/models/qwen3-4b-uncensored) [Shaz Kashani](https://savrn.com/model-publishers/ugeee) Open weights [View model](https://savrn.com/models/qwen3-4b-uncensored) Model · Text generation ### [Qwen3-4B-expert-Code](https://savrn.com/models/qwen3-4b-expert-code) [David S](https://savrn.com/model-publishers/david260928) Code generation expert. Full BF16 weights with configuration and tokenizer. Supervised fine-tuned Qwen3 weights. Apache 2.0; see LICENSE and NOTICE. Open weights apache-2.0 4B parameters 65,536 tokens transformers [View model](https://savrn.com/models/qwen3-4b-expert-code) Model · Text generation ### [Qwen3-4B-expert-Math](https://savrn.com/models/qwen3-4b-expert-math) [David S](https://savrn.com/model-publishers/david260928) Mathematical reasoning expert. Full BF16 weights with configuration and tokenizer. Supervised fine-tuned Qwen3 weights. Apache 2.0; see LICENSE and NOTICE. Open weights apache-2.0 4B parameters 65,536 tokens transformers [View model](https://savrn.com/models/qwen3-4b-expert-math) Model ### [Qwen3-4B-expert-Science](https://savrn.com/models/qwen3-4b-expert-science) [David S](https://savrn.com/model-publishers/david260928) Open weights [View model](https://savrn.com/models/qwen3-4b-expert-science) Model ### [Qwen3-4B-math-top50-lr3e-6-rolloutseed52-zfgbc](https://savrn.com/models/qwen3-4b-math-top50-lr3e-6-rolloutseed52-zfgbc) [Ruizhe Shi](https://savrn.com/model-publishers/zhezi12138) Open weights [View model](https://savrn.com/models/qwen3-4b-math-top50-lr3e-6-rolloutseed52-zfgbc) Model ### [Qwen3-4B-spec-grpo-dapo](https://savrn.com/models/qwen3-4b-spec-grpo-dapo) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/qwen3-4b-spec-grpo-dapo) Model ### [Qwen3-4B-spec-grpo-nsv-scale](https://savrn.com/models/qwen3-4b-spec-grpo-nsv-scale) [Yurun Yuan](https://savrn.com/model-publishers/yurunyyr) Open weights [View model](https://savrn.com/models/qwen3-4b-spec-grpo-nsv-scale) Model ### [Qwen3-4B-spec-grpo-sealed](https://savrn.com/models/qwen3-4b-spec-grpo-sealed) [Yurun Yuan](https://savrn.com/model-publishers/ryanyr) Open weights [View model](https://savrn.com/models/qwen3-4b-spec-grpo-sealed) Model ### [Qwen3-4B-spec-models](https://savrn.com/models/qwen3-4b-spec-models) [Yurun Yuan](https://savrn.com/model-publishers/yyuan244) Open weights [View model](https://savrn.com/models/qwen3-4b-spec-models) Model · Reinforcement learning ### [Qwen3-4B-tau2-grpo-retail-2ep-lr1e6](https://savrn.com/models/qwen3-4b-tau2-grpo-retail-2ep-lr1e6) [MinseokKang](https://savrn.com/model-publishers/mangseok123) Trained on the retail domain of tau2-bench alone rather than on airline+retail jointly, completing a four-way sweep (plain/memory x airline/retail). On retail the memory-augmented variant is the stronger of the two at this budget (avg@3 0.558 vs 0.492, pass^3 0.475 vs 0.350), and that ordering also held under joint training. Joint training carried to 5 epochs reached avg@3 0.592 (memory) and 0.558 (plain), so epoch count mattered more than domain separation here. Numbers use a 60-hop episode cap rather than tau2's 200-hop default, so they are not comparable to the public leaderboard. Open weights apache-2.0 4.4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-4b-tau2-grpo-retail-2ep-lr1e6) ## 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.