# Models, Page 391 of 477 Source: https://savrn.com/models/page/391 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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Model ### [panini-gemma-swe-bench](https://savrn.com/models/panini-gemma-swe-bench) [18kansal](https://savrn.com/model-publishers/18kansal) Open weights apache-2.0 transformers [View model](https://savrn.com/models/panini-gemma-swe-bench) Model ### [panini4b](https://savrn.com/models/panini4b) [18kansal](https://savrn.com/model-publishers/18kansal) Open weights apache-2.0 transformers [View model](https://savrn.com/models/panini4b) Model · Translation ### [pantogloss-500-en-v6-compact](https://savrn.com/models/pantogloss-500-en-v6-compact) [Chris Mattmann](https://savrn.com/model-publishers/chrismattmann) This release stores the released Pantogloss v6 fine-tune in a smaller FP16/INT5 format. Pantogloss 0.23.0 selects this model as its recommended default. The staged, checksummed runtime and legal artifacts total 564,137,246 bytes (538.0 MiB). Model weights are separate from the Python wheel. The model uses the same architecture, source and target tokenizers, and many-to-English task as the full-precision v6 release. Eight existing source languages were targeted during v6 fine-tuning; this does not add new source languages. “500-to-English” describes model-family provenance, not uniformly validated quality across 500 languages. The evaluations below cover 50. For Apple Metal, use… Open weights apache-2.0 pantogloss [View model](https://savrn.com/models/pantogloss-500-en-v6-compact) Model ### [paper-embodied-ai](https://savrn.com/models/paper-embodied-ai) [Bartosz G. Kowalczyk](https://savrn.com/model-publishers/bartoszokowalczyk) This repository contains a working research note about Embodied AI. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should… Open weights cc-by-4.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/paper-embodied-ai) Model · Reinforcement learning ### [par-quad-pinwheel-mitq-e2e-v1-4M](https://savrn.com/models/par-quad-pinwheel-mitq-e2e-v1-4m) [Arjun Narayanan](https://savrn.com/model-publishers/arjunnarayanan) The released agent from Playing to Par: Reinforcement Learning for Provably Optimal Quadrilateral Block Decompositions (Arjun Narayanan and Per-Olof Persson, UC Berkeley). The agent builds a quadrilateral block decomposition from a 2D domain's bare boundary. It aims for par: the lower bound on vertex irregularity that the discrete Gauss–Bonnet identity fixes from the domain's corner angles and topology alone, I(M) ≥ par(Ω) = |c(Ω) + 4χ|. A mesh that reaches par is provably optimal in its connectivity. into demonstrations), followed by 4,004,096 PPO steps. edge list) that follows each half-edge's next, previous and twin. It has 4 layers of 96 features on 10 input features per half-edge.… Open weights mit stable-baselines3 [View model](https://savrn.com/models/par-quad-pinwheel-mitq-e2e-v1-4m) Model · Text to speech ### [paradee-8m-glade](https://savrn.com/models/paradee-8m-glade) [Josh Leverette](https://savrn.com/model-publishers/coder543) Mixed-precision conversion of Paradee-8M v1.0, a single-voice English distillation of Kokoro. It accepts phonemes and generates mono 24 kHz Float PCM with its learned afheart voice. Glade is a native Swift library for speech inference on Apple silicon, using Core AI, Core ML, and model-specific host implementations. Its runtime source is currently private. This repository distributes the model assets and runtime metadata used by that library. Python is export/measurement tooling, not an application dependency. Mac qualification is included; iPhone and Watch qualification is not claimed. Download weights and configuration from one snapshot. INT8 text/prosody/acoustic weights use FP16… Open weights apache-2.0 [View model](https://savrn.com/models/paradee-8m-glade) Model ### [parakeet-ctc-0.6b-onnx](https://savrn.com/models/parakeet-ctc-0-6b-onnx) [Allen Programz](https://savrn.com/model-publishers/ai-pro11) Open weights [View model](https://savrn.com/models/parakeet-ctc-0-6b-onnx) Model · Speech recognition ### [parakeet-redux-gguf](https://savrn.com/models/parakeet-redux-gguf) [Dorian](https://savrn.com/model-publishers/nairod785) GGUF files of moondream/parakeet-redux for transcribe.cpp, keeping the model ternary: the encoder weights stay {−1, 0, +1} codes with one scale per 128 weights (1.75 bits/weight) and run on dedicated ternary kernels. Nothing is dequantized on disk. Parakeet Redux is Moondream's ternary sibling of parakeet-ultra, itself a post-training of NVIDIA's parakeet-tdt-0.6b-v3: FastConformer encoder + TDT transducer decoder, 25 European languages, 16 kHz mono input, punctuated and cased output with optional token/word timestamps. Not a streaming model; does not translate. - Format details, kernels, and how to reproduce: QUANTIZATION.md The ternary encoder weights (604 M, 1.75 bits/weight) are… Open weights cc-by-4.0 transcribe.cpp [View model](https://savrn.com/models/parakeet-redux-gguf) Model · Speech recognition ### [parakeet-tdt_ctc-0.6b-ja-onnx-int8](https://savrn.com/models/parakeet-tdt-ctc-0-6b-ja-onnx-int8) [Penta2himajin](https://savrn.com/model-publishers/penta2himajin) An INT8 ONNX build of nvidia/parakeet-tdtctc-0.6b-ja (TDT branch). It is the Japanese ASR model used by euhadra. Only the encoder's constant-weight MatMuls are quantised. On FLEURS-ja this keeps the CER of the FP32 export. A build that quantises every op loses 35 %. On the 321 utterances, this build's hypothesis is identical to FP32's on 257. Of the rest, 21 score worse and 23 better. Measurements and discussion: euhadra#173. The layout is the one parakeet-rs-featsize loads: it looks for encoder-model.onnx, then encoder-model.int8.onnx. Don't put FP32 encoder files in the same directory. 1. Original model: nvidia/parakeet-tdtctc-0.6b-ja, revision 44edb27eea9317daf89333e75eb830db4b1cc298… Open weights cc-by-4.0 onnx [View model](https://savrn.com/models/parakeet-tdt-ctc-0-6b-ja-onnx-int8) Model · Speech recognition ### [parakeet-ultra-gguf](https://savrn.com/models/parakeet-ultra-gguf) [Dorian](https://savrn.com/model-publishers/nairod785) GGUF quantizations of moondream/parakeet-ultra for transcribe.cpp. Parakeet Ultra is Moondream's full-precision post-training of NVIDIA's parakeet-tdt-0.6b-v3: the same FastConformer encoder (24 layers, dmodel 1024) and TDT transducer decoder, the same 25-language 8192-piece vocabulary, with better accuracy than v3 across English, European languages, noisy audio and long-form speech (upstream figures below). It takes 16 kHz mono audio and returns a punctuated, cased transcript with optional token/word timestamps. It is not a streaming model and does not translate. - How these files were made and how to reproduce them: QUANTIZATION.md Every file is at most the size of the parent checkpoint… Open weights cc-by-4.0 transcribe.cpp [View model](https://savrn.com/models/parakeet-ultra-gguf) Model · Speech recognition ### [parakeet-ultra-glade](https://savrn.com/models/parakeet-ultra-glade) [Josh Leverette](https://savrn.com/model-publishers/coder543) Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade conversion of Moondream Parakeet Ultra, a post-trained NVIDIA Parakeet TDT 0.6B V3 model. Source revision: 73175eb7aeb0d82f1e2a6b53b3aabc10a90bcd0b. The weights retain the upstream CC-BY-4.0 license. See LICENSE and NOTICE. This conversion is not endorsed by Moondream or NVIDIA. The repository contains one self-contained bundle. It preserves the model's native speech head to find pauses, selecting chunks of at most 30 seconds. All audio is retained… Open weights cc-by-4.0 [View model](https://savrn.com/models/parakeet-ultra-glade) Model · Speech recognition ### [parakeet-unified-ONNX](https://savrn.com/models/parakeet-unified-onnx) [Mrfakename](https://savrn.com/model-publishers/mrfakename) ONNX files for nvidia/parakeet-unified-en-0.6b (also mirrored at algorithco/parakeet-unified-en-0.6b), sized for ONNX Runtime Web with WebGPU. They are used by the parakeet-unified-webgpu Space. The int8 decoder is taken from the same repo. - The 48 pointwise (kernel size 1) Conv1d layers were rewritten as MatMul so that the weight-only quantiser covers them. Then the encoder was quantised with ONNX Runtime's MatMulNBitsQuantizer. - preprocessor.onnx is the same NeMo 128-mel front end used in mrfakename/parakeet-ultra-ONNX, with the same settings as this model. The encoder uses full-context attention, which is the model's offline mode. The chunked streaming attention masks are not part of… Open weights other onnx [View model](https://savrn.com/models/parakeet-unified-onnx) Model · Speech recognition ### [parakeet-v2-glade](https://savrn.com/models/parakeet-v2-glade) [Josh Leverette](https://savrn.com/model-publishers/coder543) Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade conversion of NVIDIA Parakeet V2, pinned to ae9ad07059c7c739ffaf932226a8fe64ae2620b0. Converted weights retain the upstream Creative Commons Attribution 4.0 license. See LICENSE and NOTICE. This conversion is not endorsed by NVIDIA. - fast/: full finite Fourier relative attention, 192 encoder positions, fixed 15-second chunks, and an FP16 ANE decoder batching up to 128 independent chunks. - quality/: full sinusoidal relative attention, shared weights for… Open weights cc-by-4.0 [View model](https://savrn.com/models/parakeet-v2-glade) Model · Speech recognition ### [parakeet-v3-glade](https://savrn.com/models/parakeet-v3-glade) [Josh Leverette](https://savrn.com/model-publishers/coder543) Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. Glade conversion of NVIDIA Parakeet V3, pinned to 541d1f99c6b0c3cd0b11a95167540bb8edefd82b. Converted weights retain the upstream Creative Commons Attribution 4.0 license. See LICENSE and NOTICE. This conversion is not endorsed by NVIDIA. - fast/: full finite Fourier relative attention, 192 encoder positions, fixed 15-second chunks, and an FP16 ANE decoder batching up to 128 independent chunks. - quality/: full sinusoidal relative attention, shared weights for… Open weights cc-by-4.0 [View model](https://savrn.com/models/parakeet-v3-glade) Model · Text classification ### [parallax-2-preview](https://savrn.com/models/parallax-2-preview) [Parallax Labs](https://savrn.com/model-publishers/parallaxlabs) Parallax 2 Preview is the first public look at Parallax Labs' upcoming Parallax-2 model. It's a 4B finetune of Qwen 3.5, trained specifically to reason through problems carefully, like a frontier model would. Parallax-2-preview is a 4B parameter finetune designed to demonstrate structured reasoning at small scale. It uses the reasoning style of frontier models, while remaining fast enough to run on consumer hardware. Parallax 2 Preview is intended for reasoning-heavy tasks where a short, direct answer isn't the goal. Suitable for further finetuning into reasoning-focused applications, education tools, or as a component in multi-model pipelines where structured reasoning is the required… Open weights apache-2.0 [View model](https://savrn.com/models/parallax-2-preview) Model ### [parc-ckpts](https://savrn.com/models/parc-ckpts) [Jun Shimokawatoko](https://savrn.com/model-publishers/shimokawatoko) Open weights [View model](https://savrn.com/models/parc-ckpts) Model ### [parc2026-smolvla-dbowl-lora](https://savrn.com/models/parc2026-smolvla-dbowl-lora) [Yuya Takeda](https://savrn.com/model-publishers/rutilo) Open weights [View model](https://savrn.com/models/parc2026-smolvla-dbowl-lora) Model ### [parc2026-suite-moe-adapters](https://savrn.com/models/parc2026-suite-moe-adapters) [Kaito Furuo](https://savrn.com/model-publishers/kaito-f) Open weights [View model](https://savrn.com/models/parc2026-suite-moe-adapters) Model ### [particle-qwen35-ngram-research](https://savrn.com/models/particle-qwen35-ngram-research) [Kineticforge](https://savrn.com/model-publishers/kineticforge) A learned lookup-memory adapter adds 273 MB to Qwen3.5-0.8B. We compare it with the unmodified model, same-data LoRA, and stock Qwen3.5-2B. 50 questions per model per benchmark; thinking enabled. Scores are correct answers, using the same questions for each model. - A tradeoff, not a general upgrade: memory scores higher than stock 0.8B on coding (9 vs 4) and instructions (32 vs 27), nearly the same on reasoning (18 vs 17), and lower on math (16 vs 25 under strict scoring). - The gains need confirmation. Memory changes which questions succeed; the paired intervals below show the uncertainty in the net gains. - It does not replace a 2B model in these tests. Stock 2B leads all four panels.… Open weights apache-2.0 pytorch [View model](https://savrn.com/models/particle-qwen35-ngram-research) Model ### [patch-shuffle-structural-consensus-4b-step-60](https://savrn.com/models/patch-shuffle-structural-consensus-4b-step-60) [Yihao Quan](https://savrn.com/model-publishers/0x33b) Open weights 5.2B parameters 262,144 tokens [View model](https://savrn.com/models/patch-shuffle-structural-consensus-4b-step-60) Model ### [patchtst-wavelet-sp500-research](https://savrn.com/models/patchtst-wavelet-sp500-research) [Alex Goldenstein](https://savrn.com/model-publishers/goldale) OHLCV + MA5 + MA23 + MA53 + RSI + MACD + VIX + MAVOL = 12 channels Multi-scale moving averages, RSI, MACD, VIX, and volume smoothing — none break 50% DA. Cross-sectional ranking (not direction prediction): - Signal is in slow factors (60-day volatility, momentum), not daily direction 1. DA(diff) ≈ 70% is fake — always use DA(ctx) 2. Global wavelet = look-ahead bias — v3's 53.3% was 100% from leakage 3. More features = overfitting — v4 (25ch) complex — LightGBM (2 sec) > PatchTST (hours) > Kronos (102M params) - notebooks/ — v3-v6 Colab training notebooks - results/ — v3-v6 results JSON files - PROJECTCONCLUSION.md — Full findings + future work - CLAUDE.md — Agent reference (lessons… Open weights 602,497 parameters [View model](https://savrn.com/models/patchtst-wavelet-sp500-research) Model · Feature extraction ### [patent_retriever_multilingual_nemotron_v0.1](https://savrn.com/models/patent-retriever-multilingual-nemotron-v0-1) [Elplaguister](https://savrn.com/model-publishers/elplaguister) Dense retriever for patent prior-art search (abstract → abstract), fine-tuned from on patent-family pairs (same invention filed in different jurisdictions / languages). Trained for Korean ↔ English cross-lingual and monolingual patent retrieval. Do not use the base model's asymmetric convention (query: / passage: ). It raises no error but silently degrades retrieval quality, because training was symmetric. corpus (other family members masked; one relevant document per query). crosssame: translation pairs (KR↔EN), crosssimilar: cross-lingual same-family non-translation pairs, monosimilar: monolingual same-family pairs. Scores on the 50k-document subset; full-corpus (209,671 documents)… Open weights other 1.1B parameters 262,144 tokens sentence-transformers [View model](https://savrn.com/models/patent-retriever-multilingual-nemotron-v0-1) Model ### [pawz-h3-models](https://savrn.com/models/pawz-h3-models) [Berke Icel](https://savrn.com/model-publishers/berkeicel) Unmodified copies of MiniMax H3 files (Comfy-Org/MiniMax-H3: pruned int8/fp8 diffusion checkpoints, nvfp4/int8 text encoders, VAEs, ref2v turbo LoRA) and the lightx2v/Minimax-h3-Turbo fl2v 4-step LoRA (Apache-2.0), bundled as one repo for a serverless model cache. MiniMax H3 files are licensed under the MiniMax H3 Community License Agreement (LICENSE-MiniMax-H3, NOTICE). Open weights other [View model](https://savrn.com/models/pawz-h3-models) Model · Object detection ### [pcb-aoi-yolo-rmutt](https://savrn.com/models/pcb-aoi-yolo-rmutt) [Pattarapon Prakodchue](https://savrn.com/model-publishers/moszer777) YOLO26 models that find 11 kinds of electronic components on printed circuit boards. They are the vision part of a PCB automated optical inspection (AOI) machine built at Rajamangala University of Technology Thanyaburi (RMUTT): a 4K camera on an XY gantry visits every point of a board, the model detects the parts in each frame, and the machine compares them with a golden reference board to decide PASS / FAIL for every part (OK, MISSING, WRONG type, EXTRA). Every other folder is an earlier experiment (25 training runs in total), kept with its args.yaml, results.csv and plots so the work can be traced. Each folder is one run: weights/best.pt, weights/last.pt, training curves, confusion… Open weights mit ultralytics [View model](https://savrn.com/models/pcb-aoi-yolo-rmutt) ## 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.