# Models, Page 254 of 512 Source: https://savrn.com/models/page/254 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. 12,283Models 3,510Datasets 290Papers 7,436Publishers 12,632Sourced relationships Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,506](https://savrn.com/models?task=Text+generation) - [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction396](https://savrn.com/models?task=Feature+extraction) - [Robotics379](https://savrn.com/models?task=Robotics) - [Text classification328](https://savrn.com/models?task=Text+classification) - [Text to image228](https://savrn.com/models?task=Text+to+image) - [Speech recognition185](https://savrn.com/models?task=Speech+recognition) - [Text to speech143](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) - [Token classification99](https://savrn.com/models?task=Token+classification) - [Reinforcement learning97](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](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) - [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) - [Video classification60](https://savrn.com/models?task=Video+classification) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,947](https://savrn.com/models?library=transformers) - [peft361](https://savrn.com/models?library=peft) - [diffusers202](https://savrn.com/models?library=diffusers) - [pytorch194](https://savrn.com/models?library=pytorch) - [gguf181](https://savrn.com/models?library=gguf) - [mlx157](https://savrn.com/models?library=mlx) - [lerobot150](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp45](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file) - [minimax-h327](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [exllamav320](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [keras15](https://savrn.com/models?library=keras) Modality - [Other7,017](https://savrn.com/models?modality=Other) - [Text2,788](https://savrn.com/models?modality=Text) - [Image and text619](https://savrn.com/models?modality=Image+and+text) - [Image593](https://savrn.com/models?modality=Image) - [Audio476](https://savrn.com/models?modality=Audio) - [Control476](https://savrn.com/models?modality=Control) - [Video115](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-2.03,285](https://savrn.com/models?license=apache-2.0) - [mit1,305](https://savrn.com/models?license=mit) - [other759](https://savrn.com/models?license=other) - [cc-by-nc-4.0209](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-4.052](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) - [unknown32](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-3.020](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 weights11,872](https://savrn.com/models?access=open_access) - [Access requested at publisher411](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai102](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) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan64](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Yurun Yuan61](https://savrn.com/models?publisher=yuruny) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Khazzz1c52](https://savrn.com/models?publisher=khazic) - [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [NVIDIA49](https://savrn.com/models?publisher=nvidia) [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) 12,283 models, sorted by most downloaded. Model · Image and text to text ### [ancient-words-o1-gguf](https://savrn.com/models/ancient-words-o1-gguf) [Rengo Syrup](https://savrn.com/model-publishers/commreteris) GGUF files of commreteris/ancient-words-o1 for llama.cpp. The model reads a scanned book page and returns its regions and every word, each with a polygon, as one JSON object. The prompt, output format, training and evaluation are described in that repository's model card. Both files are needed. They were converted from the float32 weights with llama.cpp 0c1e570 (converthftogguf.py --outtype q80 --no-mtp, and --mmproj --outtype f16). Qwen3.5 support needs a llama.cpp build from that commit or later. The fine-tuned checkpoint has no multi-token-prediction head, so these files cannot be used for MTP speculative decoding. llama.cpp needs a Hugging Face token with read access in HFTOKEN. The… Access requested at publisher apache-2.0 gguf [View model](https://savrn.com/models/ancient-words-o1-gguf) Model ### [andromeda-gguf](https://savrn.com/models/andromeda-gguf) [Enderman98](https://savrn.com/model-publishers/ender98) Open weights 12.2B parameters 131,072 tokens [View model](https://savrn.com/models/andromeda-gguf) Model ### [ani](https://savrn.com/models/ani) [Ik](https://savrn.com/model-publishers/anik3210) Open weights [View model](https://savrn.com/models/ani) Model ### [anim](https://savrn.com/models/anim) [Jelie](https://savrn.com/model-publishers/jlie) Open weights [View model](https://savrn.com/models/anim) Model · Text to image ### [anima-qnn-8elite](https://savrn.com/models/anima-qnn-8elite) [Adiiirfan](https://savrn.com/model-publishers/adiirfan01) Context binaries (QAIRT 2.50.40, HTP v79, socmodel 69) that run circlestone-labs/Anima Turbo v1.1 entirely on the Snapdragon 8 Elite neural processing unit: prompt encoder, 28-block drawing model and picture decoder. 6 picture sizes (512 and 768 class; square, 9:16, 16:9), 4/6/8 steps, about 6–7 s per 512×512 picture at 8 steps on an iQOO 13. Optional guide picture (layout/pose control) through kohya-ss/Anima-LLLite any-test-like-v2, built into the drawing model. Non-commercial use only — see LICENSE.md and NOTICE. Generated pictures may be used commercially. This is a modified version of Anima and is not an official CircleStone Labs release. Low memory set (what the app's "Low memory"… Open weights other [View model](https://savrn.com/models/anima-qnn-8elite) Model ### [anima2.9b-convrot-int8](https://savrn.com/models/anima2-9b-convrot-int8) [Lkj](https://savrn.com/model-publishers/losllwle) Open weights apache-2.0 [View model](https://savrn.com/models/anima2-9b-convrot-int8) Model ### [anima_baseV10_txt](https://savrn.com/models/anima-basev10-txt) [Diana d'Arc](https://savrn.com/model-publishers/b0rghese) Open weights [View model](https://savrn.com/models/anima-basev10-txt) Model · Text to image ### [animagine-xl-npu-8gen3](https://savrn.com/models/animagine-xl-npu-8gen3) [Tom](https://savrn.com/model-publishers/latentdivergence) Animagine XL 3.1 with the SDXL-Lightning 4-step LoRA fused in, its UNet quantised to w8a16 (int8 weights, int16 activations) with Qualcomm AI Hub and compiled to a Hexagon NPU context binary for the Snapdragon 8 Gen 3 (QAIRT 2.45). 1024x1024 in 4 steps, about 11 seconds on a Snapdragon 8 Gen 3 including load. Full-size SDXL does not fit one HTP graph in fp16 (~5.5 GB against a 3.5 GB limit); int8 weights bring the UNet to 2.6 GB. The graph has fp32 IO bound by timeids -> output0. Context binaries are per chip generation. The VAE decoder comes from latentdivergence/lcm-ssd1b-npu-, the text encoders from latentdivergence/animagine-xl-lightning-int8, the tokenizer from… Open weights openrail++ [View model](https://savrn.com/models/animagine-xl-npu-8gen3) Model ### [animecolor](https://savrn.com/models/animecolor) [Huangqianjin](https://savrn.com/model-publishers/huangdffs) Open weights apache-2.0 39M parameters diffusers [View model](https://savrn.com/models/animecolor) Model · Text to image ### [animemixv10_lcm-OpenVINO-INT4](https://savrn.com/models/animemixv10-lcm-openvino-int4) [Max Sun](https://savrn.com/model-publishers/hellosun) Source: sca255/animemixv10lcm (SD 1.5 LCM anime model, StableDiffusionPipeline). Recipe: numinferencesteps=8, guidancescale=1.5, 1024x1024 (LCM distilled model). Converted with optimum-intel 2.2.0 / optimum 2.3.0 / openvino 2026.4.0 / nncf 3.4.0: 1. optimum-cli export openvino -m sca255/animemixv10lcm --task text-to-image --library diffusers --weight-format fp16./animemixv10lcm-ov-fp16 2. OVQuantizer NNCF weight-only INT4 (bits=4, sym=False, groupsize=128, fallback=adjust, ratio=1.0) on unet + textencoder, VAE INT8 default. See quantizeint4.py. - FP16 OV 3.2G → INT4 OV 0.86G (unet 700M, textencoder 81M, vaedec 48M, vaeenc 33M, ~73%↓). Env: diffusers==0.37.1 transformers==4.57.6… Open weights openrail++ diffusers [View model](https://savrn.com/models/animemixv10-lcm-openvino-int4) Model ### [ankittestrepo](https://savrn.com/models/ankittestrepo) [Ankit Singhal](https://savrn.com/model-publishers/itforankit) Open weights [View model](https://savrn.com/models/ankittestrepo) Model ### [anlp-a2-moe_top2_same_active](https://savrn.com/models/anlp-a2-moe-top2-same-active) [Rithvik](https://savrn.com/model-publishers/iconically-mine) ffntype: moetop2sameactive - dmodel=256, nlayers=6, nheads=8, ffnhiddendim=1024 Checkpoint: moetop2sameactivefinal.pt (load with torch.load, see src/part1/model/transformer.py). Open weights [View model](https://savrn.com/models/anlp-a2-moe-top2-same-active) Model ### [anlp-a2-part2](https://savrn.com/models/anlp-a2-part2) [Sanjana Reddy Vonteri](https://savrn.com/model-publishers/sanjana-vonteri) Open weights [View model](https://savrn.com/models/anlp-a2-part2) Model ### [anlp-a2-part2-adamw](https://savrn.com/models/anlp-a2-part2-adamw) [Manav Beriwal](https://savrn.com/model-publishers/bmanav) Open weights [View model](https://savrn.com/models/anlp-a2-part2-adamw) Model ### [anlp-a2-part2-nadamw](https://savrn.com/models/anlp-a2-part2-nadamw) [Manav Beriwal](https://savrn.com/model-publishers/bmanav) Open weights [View model](https://savrn.com/models/anlp-a2-part2-nadamw) Model ### [anlp-assignment2-part1-m2-top1](https://savrn.com/models/anlp-assignment2-part1-m2-top1) [Hemang Joshi](https://savrn.com/model-publishers/karma-skz) Open weights [View model](https://savrn.com/models/anlp-assignment2-part1-m2-top1) Model ### [anlp2-part1-variant2](https://savrn.com/models/anlp2-part1-variant2) [Nani](https://savrn.com/model-publishers/nani-nav) Open weights [View model](https://savrn.com/models/anlp2-part1-variant2) Model · Text to image ### [annap](https://savrn.com/models/annap) [ADuchate](https://savrn.com/model-publishers/bouhpwet1112) A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token vxy girl to invoke the concept. Open weights apache-2.0 diffusers [View model](https://savrn.com/models/annap) Model ### [annulus-year-plugins](https://savrn.com/models/annulus-year-plugins) [Lingfei Qian](https://savrn.com/model-publishers/lfqian) Open weights [View model](https://savrn.com/models/annulus-year-plugins) Model ### [anode-models](https://savrn.com/models/anode-models) [Moh Hasbi Alfarizi](https://savrn.com/model-publishers/peanut2butter9) Open weights cc-by-nc-sa-4.0 [View model](https://savrn.com/models/anode-models) Model · Text to speech ### [antalia-mini-web-onnx](https://savrn.com/models/antalia-mini-web-onnx) [Fr0stb1rd](https://savrn.com/model-publishers/fr0stb1rd) ONNX-converted weights of cloud0day3/antalia-mini for in-browser inference (produced by fr0stb1rd/antalia-mini-web). - textstage.onnx: text → character features + durations - soundstage.onnx: DiT Flow-Matching generator → mel frames - decoder.onnx: Vocos ConvNeXt vocoder → 48 kHz waveform - foreigndict.json: CMU English to Turkish phonetic dictionary (80k+ words) - foreignparts.json: CamelCase part respellings for brand words (37k entries) - knownwords.json: frequent Turkish words (anti-respell shield, 6k entries) - englishi.json: dotted/dotless I decision list for English words (14k entries) The models in this repository are licensed under the Apache License 2.0. This software comes with… Open weights apache-2.0 onnx [View model](https://savrn.com/models/antalia-mini-web-onnx) Model · Text generation ### [antcoder-builder-7b](https://savrn.com/models/antcoder-builder-7b) [Deep Das](https://savrn.com/model-publishers/tornado9991) Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Builder-7B is a high-precision LoRA adapter fine-tuned on top of Qwen/Qwen2.5-Coder-7B-Instruct. It is specifically optimized to perform Contract-to-Implementation synthesis for complex, production-grade TypeScript applications. Given a strict TypeScript interface, class signature, function type contract, or JSDoc specification, AntCoder-Builder synthesizes the complete, strictly-typed implementation without type errors, missing properties, or hallucinated APIs. - Zero-Stub Completions (99.4%): Completely eliminates lazy // TODO, /... /, or throw new Error("not implemented") placeholders commonly emitted by… Open weights apache-2.0 peft [View model](https://savrn.com/models/antcoder-builder-7b) Model · Text generation ### [antcoder-fixer-7b](https://savrn.com/models/antcoder-fixer-7b) [Deep Das](https://savrn.com/model-publishers/tornado9991) Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Fixer-7B is a specialized LoRA adapter fine-tuned on Qwen/Qwen2.5-Coder-7B-Instruct. It solves the single hardest problem in autonomous coding agents: hallucinatory patching and compile loops. Unlike generalist models that attempt to rewrite entire 500-line source files (frequently breaking unrelated functions or losing imports), AntCoder-Fixer acts as a surgical precision tool: 1. It ingests the exact TypeScript compiler diagnostic (e.g. TS2339: Property 'user' does not exist on type 'Session'). 2. It ingests the local 20-line source context window. 3. It emits a minimal, standard Git Unified Diff patch (… Open weights apache-2.0 peft [View model](https://savrn.com/models/antcoder-fixer-7b) Model ### [antielite_classifier_bsample_010_s44](https://savrn.com/models/antielite-classifier-bsample-010-s44) [Anonymous_S](https://savrn.com/model-publishers/anonymouscs) Open weights 178M parameters 512 tokens [View model](https://savrn.com/models/antielite-classifier-bsample-010-s44) ## 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.