Original server folder: /home/work/mntvol/runs/nuripi05robottt/base80h4reg1620260922. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
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
11,442 models, sorted by most downloaded.
Original server folder: /home/work/mntvol/runs/nuripi05robottt/control80frozenttt8020260928. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/control80ttt2020260925. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/control80ttt40cont20260926. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/control80ttt80cont20260926. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/pi05baseline. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and third-party…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/public80horizon5k20260921. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and…
Original server folder: /home/work/mntvol/robottt/smoke. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and third-party components retain their…
Original server folder: /home/work/mntvol/robottt/stage1. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and third-party components retain their…
Original server folder: /home/work/mntvol/runs/nuripi05robottt/warm8020260920. Restore the contents of payload/ to that path. Contains the original experimental checkpoint/configuration/result files, including resume state when present. This is an archival snapshot, not a claim of evaluation success or an inference-ready standard model. Credentials, external links, caches and opaque tracking/profiling files are excluded and listed in BACKUPMANIFEST.json. Keep the manifest and final verification receipt. No model training or inference was performed for this backup. GR00T derivatives remain subject to the included NVIDIA research/evaluation-only license. Upstream pi/openpi and third-party…
Full training-run archive for Kuza (East Africa agricultural assistant), fine-tuned from unsloth/Qwen3.5-4B. Weights, logs, checkpoints, GGUFs, and provenance are stored with the same layout as $KUZAWORKDIR/kuza-qwen-3.5-4b/. This derivative is subject to the Apache 2.0 license of the Qwen base model. - 100% English train from kuzaai/kuzasftenglish - 35% Swahili from kuzaai/kuzasftswahili - 8% HuggingFaceH4/norobots - 5% adversarial from kuzaai/kuzasftadversarial - all multiturn from kuzaai/kuzasftmultiturn Sequence length 1024, 2 epochs, LR 0.0002. Thinking is off (--reasoning off when llama.cpp supports it). GGUFs are text-only (MTP/nextn kept; vision/audio dropped). ssmout is Q6K on…
Flags non-English words in mixed-language text. Linear heads on a frozen encoder: one for non-English words, one for likely names. Each word has character offsets, pnonenglish, nonenglishraw and namereview. Optional arguments: allowlist=[...] (words to keep as English) and threshold= (logit; default -3.68).
A family of six models that detect lashon hara in text shown on screen. Each model lives in its own folder with its own README. All six give the same kind of answer: lashonhara or notlashonhara, in English and Hebrew, for messages from short chats to long emails. They tell real lashon hara apart from look-alikes that are permitted, such as direct constructive criticism, warnings that protect someone from harm (to'eles), honest references, and discussion of the laws themselves. A screening aid, not a halachic ruling. A rav should decide real questions. pc, mobile-legacy, server and server-small are Apache 2.0. mobile-flash and mobile-lite are under the LFM Open License v1.0 (see the LICENSE…
Model · Sentence similarity
lateon-msmarco-hardneg-n10573-e1-ckpt82-qd1024
This is a PyLate model finetuned from lightonai/LateOn. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search. Load the ColBERT model and initialize the PLAID index, then encode and index your documents: Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: Once the documents are indexed, you can retrieve the top-k most relevant documents for a…
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.