# Models, Page 346 of 477 Source: https://savrn.com/models/page/346 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) - 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[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 ### [jev-qwen](https://savrn.com/models/jev-qwen) [Patrick Dugan](https://savrn.com/model-publishers/alephfunk) LoRA adapters from the Small Jev experiments (Patrick Dugan, Red Team Gladiatorics). TypeSafe's Jev is described as a "System One" model: it returns a typed, calibrated decision over a pre-enumerated output space from a single query. These adapters give an open 4B base model a single-pass, option-scored readout of that kind. They are benchmarked against compact looped decoders, TRM skill gates, script/LDT/Control-Harness gates and a 1-bit 8B LLM. This is not Jev. Jev's weights and architecture are not public; nothing here measures Jev. published, because they contain held-out evaluation items of other projects. All adapters are LoRA r16 / alpha 32 on q/k/v/o, the DeltaNet projections… Open weights apache-2.0 peft [View model](https://savrn.com/models/jev-qwen) Model ### [jev_qwen3.8-27B_svd_lora](https://savrn.com/models/jev-qwen3-8-27b-svd-lora) [Hotdogs](https://savrn.com/model-publishers/hotdogs) Open weights [View model](https://savrn.com/models/jev-qwen3-8-27b-svd-lora) Model ### [jevify-gemma-4-e4b-it-readout-coh](https://savrn.com/models/jevify-gemma-4-e4b-it-readout-coh) [Praveen Raj U S](https://savrn.com/model-publishers/praveenrajus) A System One decision model: it does not write text. It reads a state, answers typed questions (choice, score, noul), and returns calibrated probability distributions your code can branch on. This repo is a rank-16 LoRA (34,881,536 parameters) on google/gemma-4-E4B-it, merged into the weights at load. How it was trained (readout fine-tuning). The model is trained on its own decision readout — the distribution over the allowed answers read at the answer position, one forward pass, no decoding — with the primitive's proper scoring rule, plus a coherence penalty (weight 1.0): every training question comes with automatically derived siblings (the options as yes/no questions, the negation, the… Open weights apache-2.0 jevify [View model](https://savrn.com/models/jevify-gemma-4-e4b-it-readout-coh) Model ### [jevify-qwen3-vl-2b-t2](https://savrn.com/models/jevify-qwen3-vl-2b-t2) [Praveen Raj U S](https://savrn.com/model-publishers/praveenrajus) A Jevify Tier 2 model: Qwen/Qwen3-VL-2B-Instruct used as a vision-language model: state may carry images (PIL, path, URL, data URI or bytes) and the same three typed questions are asked about them, with a rank-16 LoRA confined to the decoder half of the model, trained on the readout itself — the restricted log-softmax over the allowed answers at the answer position — with the primitive's proper scoring rule, on the aokvqa train split only. The adapter (17,432,576 parameters) is merged into the backbone at load, so it serves at the plain checkpoint's speed. The calibration recipe was fitted on validation splits of the adapted model. pope, ai2d never appeared in training. Serve it as a… Open weights apache-2.0 [View model](https://savrn.com/models/jevify-qwen3-vl-2b-t2) Model ### [jevify-qwen3.5-4b-base-readout-coh](https://savrn.com/models/jevify-qwen3-5-4b-base-readout-coh) [Praveen Raj U S](https://savrn.com/model-publishers/praveenrajus) A System One decision model: it does not write text. It reads a state, answers typed questions (choice, score, noul), and returns calibrated probability distributions your code can branch on. This repo is a rank-16 LoRA (21,233,664 parameters) on Qwen/Qwen3.5-4B-Base, merged into the weights at load. How it was trained (readout fine-tuning). The model is trained on its own decision readout — the distribution over the allowed answers read at the answer position, one forward pass, no decoding — with the primitive's proper scoring rule, plus a coherence penalty (weight 1.0): every training question comes with automatically derived siblings (the options as yes/no questions, the negation, the… Open weights apache-2.0 jevify [View model](https://savrn.com/models/jevify-qwen3-5-4b-base-readout-coh) Model ### [jevify-qwen3.5-4b-readout](https://savrn.com/models/jevify-qwen3-5-4b-readout) [Praveen Raj U S](https://savrn.com/model-publishers/praveenrajus) A System One decision model: it does not write text. It reads a state, answers typed questions (choice, score, noul), and returns calibrated probability distributions your code can branch on. This repo is a rank-16 LoRA (21,233,664 parameters) on Qwen/Qwen3.5-4B, merged into the weights at load. How it was trained (readout fine-tuning). The model is trained on its own decision readout — the distribution over the allowed answers read at the answer position, one forward pass, no decoding — with the primitive's proper scoring rule. Options are shuffled per family. Training data: the train splits of the 16 non-held-out jev-bench sources (5,885 families, at most 400 records per source); lr… Open weights apache-2.0 jevify [View model](https://savrn.com/models/jevify-qwen3-5-4b-readout) Model ### [jevify-qwen3.5-4b-readout-coh](https://savrn.com/models/jevify-qwen3-5-4b-readout-coh) [Praveen Raj U S](https://savrn.com/model-publishers/praveenrajus) A System One decision model: it does not write text. It reads a state, answers typed questions (choice, score, noul), and returns calibrated probability distributions your code can branch on. This repo is a rank-16 LoRA (21,233,664 parameters) on Qwen/Qwen3.5-4B, merged into the weights at load. How it was trained (readout fine-tuning). The model is trained on its own decision readout — the distribution over the allowed answers read at the answer position, one forward pass, no decoding — with the primitive's proper scoring rule, plus a coherence penalty (weight 1.0): every training question comes with automatically derived siblings (the options as yes/no questions, the negation, the… Open weights apache-2.0 jevify [View model](https://savrn.com/models/jevify-qwen3-5-4b-readout-coh) Model · Feature extraction ### [jina-code-embeddings-0.5b-onnx](https://savrn.com/models/jina-code-embeddings-0-5b-onnx) [Nick Chapman](https://savrn.com/model-publishers/nchapman) ONNX conversion of jinaai/jina-code-embeddings-0.5b (Qwen2.5-Coder-0.5B finetune, 494M params), laid out like weights under onnx/, tokenizer + config at root, transformers.js compatible. Dynamic int8 quantization was tried (ORT quantizedynamic, QInt8, per-channel + reduce-range, with and without quantpreprocess shape inference) and scored drift ≈ 0.145 — two orders of magnitude over the 1e-3 bar. This GQA-decoder + RMSNorm + last-token-pooling architecture is far more quant-sensitive than BERT-style encoders where int8 dynamic works. Per the sentence-transformers team's guidance, the recommended efficiency path for embeddings is embedding quantization (quantize output vectors to int8/binary… Open weights cc-by-nc-4.0 32,768 tokens transformers.js [View model](https://savrn.com/models/jina-code-embeddings-0-5b-onnx) Model · Text classification ### [jiwo-0.8b-GGUF](https://savrn.com/models/jiwo-0-8b-gguf) [Breaksoftware](https://savrn.com/model-publishers/breaksoftware) I decided to use jiwo-0.8b because it had a high score on https://huggingface.co/spaces/multimodalart/jev-decision-index compared to its parameter count. This GGUF conversion / quantization was generated by a local agent powered by Qwen3.8 27B. See agent-trajectory-log.md for exhaustive details. Run with https://llama.app This is a decision model, to be used via /v1/systemone API. See https://github.com/ggml-org/llama.cpp/pull/29818 - https://huggingface.co/eljiwo/jiwo-0.8b Open weights apache-2.0 [View model](https://savrn.com/models/jiwo-0-8b-gguf) Model ### [jn1r6Mxxvv4rBBOs](https://savrn.com/models/jn1r6mxxvv4rbbos) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/jn1r6mxxvv4rbbos) Model ### [joaopedro-4bit](https://savrn.com/models/joaopedro-4bit) [Daniel Secco](https://savrn.com/model-publishers/danielseccoanubz) Open weights [View model](https://savrn.com/models/joaopedro-4bit) Model ### [joint-flow-3b](https://savrn.com/models/joint-flow-3b) [Boning Cui](https://savrn.com/model-publishers/bc-ai) Open weights [View model](https://savrn.com/models/joint-flow-3b) Model ### [jojo-sovereign-strong](https://savrn.com/models/jojo-sovereign-strong) [Mohammed Abdullah H Abdulaziz](https://savrn.com/model-publishers/dodgeqtr) This model was finetuned and converted to GGUF format using Unsloth. - Meta-Llama-3.1-8B-Instruct.Q4KM.gguf An Ollama Modelfile is included for easy deployment. The model's BOS token behavior was adjusted for GGUF compatibility. This was trained 2x faster with Unsloth Open weights 131,072 tokens [View model](https://savrn.com/models/jojo-sovereign-strong) Model ### [jonyning-PandaReachDense-v3](https://savrn.com/models/jonyning-pandareachdense-v3) [Ningmin](https://savrn.com/model-publishers/jonyning) Open weights [View model](https://savrn.com/models/jonyning-pandareachdense-v3) Model ### [jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed3407](https://savrn.com/models/jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) Open weights [View model](https://savrn.com/models/jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso-seed3407) Model · Text generation ### [jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso_seed455](https://savrn.com/models/jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso-seed455) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/ppt-wc-zipf-newlex-jpn-after-100mb-packed-bfdisoseed455. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/jpn-100mb-after-wc-zipf-newlex-after-ckpt500-packed-bfdiso-seed455) Model ### [jspace-distill-results](https://savrn.com/models/jspace-distill-results) [Arav Dhoot](https://savrn.com/model-publishers/aravdhoot) Artifacts from round 1 of J-space character distillation on google/gemma-3-12b-it (rev 96b6f1ec). Code, configs, run log and deviations: https://github.com/arav-dhoot/introspection-training/tree/jspace-distillation-arav/jspace-dist (commit 1bf6a9b); see REPRODUCE.md there. Layout mirrors the repo's results/ directory: - teacherseed0/adapters/final: LoRA teachers. TEM = teacheremfinanciallr5e-5seed0 (risky financial advice, 37.2% misaligned), control = teacherctrllr5e-5seed0. - students{1..5}seed0/adapters/{ckpt1000,ckpt5000,ckpt10000,final}: main students. - lrsweepseed0/, vseed0/ (readout-space variants, ckpt 250/500/1000). - gatee/gatee/baserefit/lens.pt (J-lens refit of the base model)… Open weights other [View model](https://savrn.com/models/jspace-distill-results) Model ### [jsxzs_model](https://savrn.com/models/jsxzs-model) [Shengxiang Ji](https://savrn.com/model-publishers/overdog) Access requested at publisher cc-by-4.0 diffusers [View model](https://savrn.com/models/jsxzs-model) Model · Text classification ### [julia-routing-strix-halo-multilingual](https://savrn.com/models/julia-routing-strix-halo-multilingual) [Richard](https://savrn.com/model-publishers/precisionailabs) Experimental multilingual fine-tune of Julia-1 for fast bounded-decision routing on AMD Strix Halo-class local machines. This is an experimental v0.2 multilingual candidate, not a replacement for the English-focused v0.1 checkpoint. - task routing over fixed options - documentation-update triage - context-management metadata - non-authoritative tool-policy hints Do not use this model as the final authority for destructive commands, credential handling, production deploys, security replay safety, durable memory writes, summarization, or final prose. By language on the large multilingual holdout: All latency numbers in development were measured on CPU. NPU acceleration has not been validated.… Open weights apache-2.0 144M parameters julia [View model](https://savrn.com/models/julia-routing-strix-halo-multilingual) Model ### [k-ty](https://savrn.com/models/k-ty) [S](https://savrn.com/model-publishers/aeon37) Open weights [View model](https://savrn.com/models/k-ty) Model ### [k3f_exp_ckpts](https://savrn.com/models/k3f-exp-ckpts) [Yang Hu](https://savrn.com/model-publishers/weilan55) Final-iteration draft weights for six single-variable arms trained against moonshotai/Kimi-K3-Flash on the GB300 cluster. Staged for batch export + config, and the draft architecture JSON. optimizer/ state is deliberately excluded — it is ~80% of the bytes and only needed to resume training. from its parent by one thing, so adjacent arms are differenceable. g3j is the one arm that varies initialisation rather than architecture: it has g3f's draft JSON byte-for-byte but starts from g3e's converged weights instead of scratch, so g3f vs g3j isolates warm-start. Note it loads block-5 weights and then trains at block 3 — the load was clean (no shape warnings in the trainer log), but serve it… Open weights other [View model](https://savrn.com/models/k3f-exp-ckpts) Model ### [k6-onnx-int8](https://savrn.com/models/k6-onnx-int8) [Lauro Maffei](https://savrn.com/model-publishers/lauro00000) ONNX int8 export of kyutai/pocket-tts, six bundles (german2026-10, english2026-09, french2026-10, italian2026-10, spanish2026-10, portuguese2026-10). Source: kyutai/pocket-tts @ 3e82814a68665eec246ff649b14c71331f955c06 (languages/ /model.safetensors). The model weights are © Kyutai Labs, licensed under CC BY 4.0 (inherited from kyutai/pocket-tts). The ONNX conversion and the quantization are the only changes. Please credit Kyutai Labs when you use these files. Open weights cc-by-4.0 onnx [View model](https://savrn.com/models/k6-onnx-int8) Model ### [k7m2qx9wt4bnzp3hvd8sr6yf](https://savrn.com/models/k7m2qx9wt4bnzp3hvd8sr6yf) [Mike Well](https://savrn.com/model-publishers/mikeone) Open weights [View model](https://savrn.com/models/k7m2qx9wt4bnzp3hvd8sr6yf) Model ### [kab-tts-piper-multispeaker](https://savrn.com/models/kab-tts-piper-multispeaker) [Kmerakeb](https://savrn.com/model-publishers/kmerakeb) Open weights [View model](https://savrn.com/models/kab-tts-piper-multispeaker) ## 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.