This repository provides RKNN deployment configurations of yolov5n-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/yolov5. The upstream license is GNU GPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.
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
12,283 models, sorted by most downloaded.
This repository provides RKNN deployment configurations of yolov5s-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/yolov5. The upstream license is GNU GPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.
This repository provides RKNN deployment configurations of yolov8m-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/ultralyticsyolov8. The upstream license is GNU AGPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.
This repository provides RKNN deployment configurations of yolov8n-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/ultralyticsyolov8. The upstream license is GNU AGPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.
This repository provides RKNN deployment configurations of yolov8s-seg for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. The model is provided through RKNN Model Zoo and originates from airockchip/ultralyticsyolov8. The upstream license is GNU AGPL v3; see LICENSE. Original copyright and attribution notices are retained in the accompanying license file. This distribution converts the source model to RKNN format for RK3576, using the precision specified in each configuration.
This repository provides RKNN deployment configurations of LightGlue for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. Source project: LightGlue. License: apache-2.0; see LICENSE. This distribution contains RKNN conversion derivatives for RK3576.
This repository provides RKNN deployment configurations of clip-vit-base-patch32 for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. Source project: clip-vit-base-patch32. License: mit; see LICENSE. This distribution contains RKNN conversion derivatives for RK3576.
This repository provides RKNN deployment configurations of dinov2vitb14 for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. Source project: dinov2vitb14. License: apache-2.0; see LICENSE. This distribution contains RKNN conversion derivatives for RK3576. See NOTICE for attribution.
This repository provides RKNN deployment configurations of dinov2vits14 for RK3576. From the directory where you ran the download command, enter the configuration directory and verify: All entries must report OK before deployment. - Supported chips are listed per configuration above. - Use the matching files from the same configuration. Source project: dinov2vits14. License: apache-2.0; see LICENSE. This distribution contains RKNN conversion derivatives for RK3576. See NOTICE for attribution.
We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. Qwen3-4B-Instruct-2507 has the following features: NOTE: This model supports only non-thinking…
SourceHat RLCD is a research model for bounded decisions: commit to one complete object, return alternatives, or abstain. A frozen Qwen3.5-9B text backbone represents context, evidence, and supplied choices in one shared forward pass. Small learned heads score the choices and their combinations; a calibrated policy selects an operating mode. This is not a conversational model or an arbitrary JSON generator. It is designed for classification and routing experiments where the caller defines the candidate choices, validates the result, and retains a verifier or human fallback. It executes no external action. On the main synthetic test, 46 of 49 commitments were correct, out of 250 total cases…
"Experimental Mistral Nemo Mix" using... "Model Stock merge with Mergekit". This is a mix of some known "RP Uncensored" models. I'm not responsible of the use of this. After noticing a kind of "Assistant Tone" in V10, i remade the mix using "a cleaner" list... It works pretty nice and stable, and it's the "Base" for something even more experimental. TheDrummer/Rocinante-X-12B-v1 as base. The Tokenizer & Embedding Sizes (131072) are clean + It's "supposed to" support now a full "128K Context Size" (patched at 1M). The chat template is added with "full mistral" support. Quantized Model, Here.
weighted/imatrix quants of https://huggingface.co/RicardoEstep/RPBizkit-v11-12B-Base For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/RPBizkit-v11-12B-Base-GGUF 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 lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9…
"Experimental Mistral Nemo Mix" using... "Model Stock merge with Mergekit". This is a mix of some known "RP Uncensored" models. I'm not responsible of the use of this. Take it as a "pure experiment", that works. RicardoEstep/RPBizkit-v11-12B-Base as base. The Tokenizer & Embedding Sizes (131072) are clean + This version "supposed to" support a full "512K+ Context Size" (patched at 1M). The chat template is the same as "heretic" one. Quantized Model, Here.
weighted/imatrix quants of https://huggingface.co/RicardoEstep/RPBizkit-v11-12B-Thinking For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/RPBizkit-v11-12B-Thinking-GGUF 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 lower-quality quant And here are Artefact2's thoughts on the matter…
"Experimental Mistral Nemo Mix" using... "Model Stock + SLERP merges with Mergekit". This is my "last version", and actually the most stable one. This is a mix of some known "RP Uncensored" models. I'm not responsible of the use of this. After noticing the fg "chaotic" last version, that works at worst... I decided to go "even simplier". I had to reduce the overall models as some are Mixes from a Mix, i tried my best using only "FineTunes". natong19/Mistral-Nemo-Instruct-2407-abliterated # Base! natong19/Mistral-Nemo-Instruct-2407-abliterated # Base!.\mergekitconfig1 # RP Core.\mergekitconfig2 # Substance The Tokenizer & Embedding Sizes (131072) are clean + It's "supposed to" support now a…
This model was converted to GGUF format from RicardoEstep/RPBizkit-v8-12B using llama.cpp via "MY LOCAL COMPUTER".
weighted/imatrix quants of https://huggingface.co/RicardoEstep/RPBizkitRemiX-v2-12B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/RPBizkitRemiX-v2-12B-GGUF 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 lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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.
Collection · 4 entries
Models that fit 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.
Collection · 6 entries
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.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.