# Models, Page 332 of 489 Source: https://savrn.com/models/page/332 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](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 learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](https://savrn.com/models?task=Image+segmentation) - [Object detection86](https://savrn.com/models?task=Object+detection) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [Translation79](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask69](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [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) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [diffusion-single-file28](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) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](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-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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) - [unknown29](https://savrn.com/models?license=unknown) - [llama3.128](https://savrn.com/models?license=llama3.1) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.017](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-m13](https://savrn.com/models?license=creativeml-openrail-m) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](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 Pu71](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) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [gc-sg-ip2p-v6pt3](https://savrn.com/models/gc-sg-ip2p-v6pt3) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights 860M parameters diffusers [View model](https://savrn.com/models/gc-sg-ip2p-v6pt3) Model ### [gc-sg-vlm-v1](https://savrn.com/models/gc-sg-vlm-v1) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights 860M parameters diffusers [View model](https://savrn.com/models/gc-sg-vlm-v1) Model ### [gc_pt2_pi05-ckpts](https://savrn.com/models/gc-pt2-pi05-ckpts) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights [View model](https://savrn.com/models/gc-pt2-pi05-ckpts) Model ### [gc_r1_pi05](https://savrn.com/models/gc-r1-pi05) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights 4.1B parameters [View model](https://savrn.com/models/gc-r1-pi05) Model ### [gc_sg1_pi05-ckpts](https://savrn.com/models/gc-sg1-pi05-ckpts) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights [View model](https://savrn.com/models/gc-sg1-pi05-ckpts) Model ### [gc_sg4_pi05](https://savrn.com/models/gc-sg4-pi05) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights 4.1B parameters [View model](https://savrn.com/models/gc-sg4-pi05) Model ### [gc_sg4_pi05-ckpts](https://savrn.com/models/gc-sg4-pi05-ckpts) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights [View model](https://savrn.com/models/gc-sg4-pi05-ckpts) Model ### [gdpo-math-qwen3-4b-thinking2507-v07-seed0-matched150](https://savrn.com/models/gdpo-math-qwen3-4b-thinking2507-v07-seed0-matched150) [Zhan](https://savrn.com/model-publishers/luvtong) Open weights [View model](https://savrn.com/models/gdpo-math-qwen3-4b-thinking2507-v07-seed0-matched150) Model ### [gelatwo-common-gen-dense-4096](https://savrn.com/models/gelatwo-common-gen-dense-4096) [Meihua Dang](https://savrn.com/model-publishers/mhdang) A hidden Markov model distilled from mhdang/gelatwo-common-gen-gpt2-large, used as the tractable proposal for keyword-constrained generation on CommonGen in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). Locally constrained decoding (LCD) enforces a constraint by masking next tokens one step at a time, which biases the samples it draws. P-GCD instead combines this HMM with a tensorized finite automaton to form a proposal that carries both the logical and the probabilistic information about the target distribution, and samples from it with sequential Monte Carlo. The HMM supplies the lookahead the This is not a fine-tune of the base LM and shares… Open weights mit 223M parameters [View model](https://savrn.com/models/gelatwo-common-gen-dense-4096) Model · Text generation ### [gelatwo-common-gen-gpt2-large](https://savrn.com/models/gelatwo-common-gen-gpt2-large) [Meihua Dang](https://savrn.com/model-publishers/mhdang) gpt2-large fine-tuned on CommonGen, used as the base language model for the CommonGen experiments in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). This is a plain causal language model: it supplies the base distribution that GCD and P-GCD steer. The tractable proposal it is paired with is the HMM at which shares its 50257-token vocabulary. configs/common-gen.yaml in github.com/MhDang/gelatwo already points at this checkpoint, so the CommonGen runs need no override. Open weights mit 774M parameters [View model](https://savrn.com/models/gelatwo-common-gen-gpt2-large) Model ### [gelatwo-spider-monarch-1024](https://savrn.com/models/gelatwo-spider-monarch-1024) [Meihua Dang](https://savrn.com/model-publishers/mhdang) A hidden Markov model distilled from meta-llama/Llama-3.1-8B-Instruct, used as the tractable proposal for SQL-constrained text-to-SQL on Spider in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). Locally constrained decoding (LCD) enforces a constraint by masking next tokens one step at a time, which biases the samples it draws. P-GCD instead combines this HMM with a tensorized finite automaton to form a proposal that carries both the logical and the probabilistic information about the target distribution, and samples from it with sequential Monte Carlo. The HMM supplies the lookahead the This is not a fine-tune of the base LM and shares none of… Open weights mit 131M parameters [View model](https://savrn.com/models/gelatwo-spider-monarch-1024) Model ### [gelatwo-xlam-function-monarch-16384](https://savrn.com/models/gelatwo-xlam-function-monarch-16384) [Meihua Dang](https://savrn.com/model-publishers/mhdang) A hidden Markov model distilled from meta-llama/Llama-3.1-8B-Instruct, used as the tractable proposal for constrained tool calling on xLAM (function-call template) in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). Locally constrained decoding (LCD) enforces a constraint by masking next tokens one step at a time, which biases the samples it draws. P-GCD instead combines this HMM with a tensorized finite automaton to form a proposal that carries both the logical and the probabilistic information about the target distribution, and samples from it with sequential Monte Carlo. The HMM supplies the lookahead the This is not a fine-tune of the base LM… Open weights mit 2.1B parameters [View model](https://savrn.com/models/gelatwo-xlam-function-monarch-16384) Model ### [gelatwo-xlam-json-monarch-16384](https://savrn.com/models/gelatwo-xlam-json-monarch-16384) [Meihua Dang](https://savrn.com/model-publishers/mhdang) A hidden Markov model distilled from meta-llama/Llama-3.1-8B-Instruct, used as the tractable proposal for constrained tool calling on xLAM (JSON template) in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). Locally constrained decoding (LCD) enforces a constraint by masking next tokens one step at a time, which biases the samples it draws. P-GCD instead combines this HMM with a tensorized finite automaton to form a proposal that carries both the logical and the probabilistic information about the target distribution, and samples from it with sequential Monte Carlo. The HMM supplies the lookahead the This is not a fine-tune of the base LM and shares… Open weights mit 2.1B parameters [View model](https://savrn.com/models/gelatwo-xlam-json-monarch-16384) Model ### [gemini](https://savrn.com/models/gemini) [Eee](https://savrn.com/model-publishers/hhrrjk) Open weights [View model](https://savrn.com/models/gemini) Model ### [gemma](https://savrn.com/models/gemma) [Donghan li](https://savrn.com/model-publishers/donghanark) Open weights gemma [View model](https://savrn.com/models/gemma) Model · Text generation ### [gemma-3-12b-it-matura-history-sft-grpo-closed-lora-r32](https://savrn.com/models/gemma-3-12b-it-matura-history-sft-grpo-closed-lora-r32) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) LoRA adapter for google/gemma-3-12b-it trained to answer the Polish history matura (poziom rozszerzony, CKE). Built at the Warsaw Model Trainers Hackathon (team repo: WarsawModelTrainersHackathon, src/posttrain/). SFT (QLoRA r=32 on the mixed matura set, no thinking; repo gemma-3-12b-it-matura-history-sft-qlora-r32), then GRPO with Unsloth + TRL on closed exam items with an exact key (single choice, true/false, matching, ordering). Reward = 0.2format + 0.8exact match, Dr.GRPO loss, beta=0 (no KL). THIS VERSION: GRPO step 25 (interim); the full 300-step run replaces it when it finishes. Closed-question exact match on validation (321 items, no judge): 68.2 overall (SFT alone 66.4, base 45.5).… Open weights gemma peft [View model](https://savrn.com/models/gemma-3-12b-it-matura-history-sft-grpo-closed-lora-r32) Model · Text generation ### [gemma-3-12b-it-matura-history-sft-qlora-r32](https://savrn.com/models/gemma-3-12b-it-matura-history-sft-qlora-r32) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) LoRA adapter for google/gemma-3-12b-it trained to answer the Polish history matura (poziom rozszerzony, CKE). Built at the Warsaw Model Trainers Hackathon (team repo: WarsawModelTrainersHackathon, src/posttrain/). Supervised fine-tuning (QLoRA) of Gemma 3 12B-it on the same mixed set; Gemma 3 has no thinking mode, so teacher traces contribute their final answers only. LoRA r=32, alpha=64, lr 1e-4 cosine, 1 epoch, batch 16, maxlen 6144, Unsloth + TRL Consolidated training set data/trainingdatasets/sfttrain.jsonl (buildsft.py): real CKE papers 2005–2026 with keys, rephrased papers, synthetic image tasks, synthetic full papers, e-podręczniki textbook questions, synthetic Claude/DeepSeek tasks… Open weights gemma peft [View model](https://savrn.com/models/gemma-3-12b-it-matura-history-sft-qlora-r32) Model ### [gemma-3-12b-rep10-intrinsic-sdf](https://savrn.com/models/gemma-3-12b-rep10-intrinsic-sdf) [Joshua Fonseca](https://savrn.com/model-publishers/joshycodes) Research checkpoint. google/gemma-3-12b-it was continued-pretrained (full weights, lr 1e-05, 1 epoch, 18,217,396 tokens, 22,351 documents, of which 0 self-authored and 22,351 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: joshycodes/qwen-constitutional-sdf-corpus. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy. Open weights other 13.2B parameters 131,072 tokens [View model](https://savrn.com/models/gemma-3-12b-rep10-intrinsic-sdf) Model ### [gemma-3-1b-aliberated](https://savrn.com/models/gemma-3-1b-aliberated) [Trey Massey](https://savrn.com/model-publishers/trey2016) Open weights gemma [View model](https://savrn.com/models/gemma-3-1b-aliberated) Model · Text generation ### [gemma-3-270m-african-diacritizer](https://savrn.com/models/gemma-3-270m-african-diacritizer) [Aletheia AI](https://savrn.com/model-publishers/aletheiaaing) Open weights gemma 268M parameters transformers [View model](https://savrn.com/models/gemma-3-270m-african-diacritizer) Model ### [gemma-3-4b-it-activity_dancing-STEER1.5-ft4.42](https://savrn.com/models/gemma-3-4b-it-activity-dancing-steer1-5-ft4-42) [Yanan He](https://savrn.com/model-publishers/echoo113) This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/gemma-3-4b-it-activity-dancing-steer1-5-ft4-42) Model ### [gemma-3-4b-it-activity_dancing_prompted-ft4.42](https://savrn.com/models/gemma-3-4b-it-activity-dancing-prompted-ft4-42) [Yanan He](https://savrn.com/model-publishers/echoo113) Open weights [View model](https://savrn.com/models/gemma-3-4b-it-activity-dancing-prompted-ft4-42) Model ### [gemma-3-4b-it-color_purple_prompted-ft4.42](https://savrn.com/models/gemma-3-4b-it-color-purple-prompted-ft4-42) [Yanan He](https://savrn.com/model-publishers/echoo113) This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/gemma-3-4b-it-color-purple-prompted-ft4-42) Model ### [gemma-3-4b-it-dragon_prompted-ft4.42](https://savrn.com/models/gemma-3-4b-it-dragon-prompted-ft4-42) [Yanan He](https://savrn.com/model-publishers/echoo113) This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/gemma-3-4b-it-dragon-prompted-ft4-42) ## 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.