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.
Updated 2026-10-18 · How the library is built
11,122 models, sorted by most downloaded.
Owner Task26 (01a0fdaa). One authorized c43 train instance 3 on centre RTX 4090 (run token centre-t26c43-i3-20261007T015937Z, dispatched 2026-10-07T01:59:37Z, C43RUNEND status=0 at 03:04:06Z). Official BEHAVIOR-1K 3.9.3 runtime; frozen candidate fb47bfb2325b2c83e86b66c78cfea4d6272d1cb4, bundle SHA256SUMS a0387569a8d067886ba190e5a43ac89b3b0f5939d175401c16166c69fa7144b3. Result: assemblinggiftbaskets, instance 3, rollout 0: success=false, qscore 0.625, 39091 steps. Contents: the run directory (official JSON, video, evaluator/policy logs, detector health, source + sourcesha256.txt), the launch record directory (source/model/dependency checks, preflight, setup log), the launch log and the c43…
Owner Task73 (current owner, Claude session ec55b5c0). One authorized C29 publictest run on centre RTX 4090 (run token centre-run-c29-20261009T022729Z, start 2026-10-09T02:27:29Z). Official BEHAVIOR-1K v3.9.2 runtime, b1979916ec15. Frozen source archive sha256 d550d25f394bf2d713ef5ab4dc76598d5242f070561af3c8930d8caefeeb3028, manifest 60f6964cb4558012146d31fa5026b95230322929bf4cf7d1e0f59594daeddb03. Result: instance=311 rollout=0 steps=21888 success=False qscore=0.8333333333333334 Contents: run dir (official JSON, video, evaluator/policy/preflight logs, identity), launcher log, source.tgz, manifest.sha256, manifest and CPU check logs. Files listed in SHA256SUMS (README.md not included).…
Owner Task73 (current owner, Claude session ec55b5c0). One authorized C29 publictest run on centre RTX 4090 (run token centre-run-c29-20261009T030115Z, start 2026-10-09T03:01:15Z). Official BEHAVIOR-1K v3.9.2 runtime, b1979916ec15. Frozen source archive sha256 d550d25f394bf2d713ef5ab4dc76598d5242f070561af3c8930d8caefeeb3028, manifest 60f6964cb4558012146d31fa5026b95230322929bf4cf7d1e0f59594daeddb03. Result: instance=320 rollout=0 steps=21888 success=False qscore=0.6666666666666666 Contents: run dir (official JSON, video, evaluator/policy/preflight logs, identity), launcher log, source.tgz, manifest.sha256, manifest and CPU check logs. Files listed in SHA256SUMS (README.md not included).…
Companion checkpoints for Adaptive WavLM-HuBERT Fusion Mechanism for Cross-Lingual revision). Start with MANIFEST.csv — one row per checkpoint, saying in plain English what model it is, which data it was trained on, which seed, what the file is for, and whether it is worth keeping. 136 of 473 files are final models; 229 are disposable intermediate state (183 GB). Paths mirror the run directory tree on the compute server. That is deliberate: the sync tool decides "already uploaded, safe to delete locally" by comparing the repo path and size against the local file, so renaming anything here would make every file look un-uploaded and trigger a full re-upload of the repo. The meaning lives in…
This model was converted to OpenVINO from John6666/tame-pony-the-authenticity-machine-v25-sdxl using optimum-intel via the export space. First make sure you have optimum-intel installed: To load your model you can do as follows
This model is a quantized version of John6666/tame-pony-the-authenticity-machine-v25-sdxl and is converted to the OpenVINO format. This model was obtained via the nncf-quantization space with optimum-intel. First make sure you have optimum-intel installed: To load your model you can do as follows
108 train / 36 val / 36 test, авторская разметка ассистентом. Validation содержит те же сценарии, что train, с другими формулировками. Test не использовался в обучении. 4 эпохи, seed 42, RTX 4060 8 GB, 599 секунд, NF4 + bf16, LoRA r=16. Независимая разметка и два API-судьи не выполнены. Полное ТЗ не закрыто. См. report.md, run.json и selectionstate.json. Это адаптер, базовые веса скачиваются отдельно. Python 3.11, Linux; pip install -r requirements.txt. Сохраните код ниже как demo.py. CPU/другая точность не воспроизводят опубликованную конфигурацию оценки. https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
This is an ONNX version of tanaos/tanaos-text-summarization-v1. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for summarization: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.SummarizationPipeline This model was created by Tanaos with the Artifex Python library. This is an abstractive text summarization model based on facebook/bart-base and fine-tuned on a synthetic dataset to produce concise, fluent summaries of longer texts. The model uses beam search decoding and is optimized for general-purpose summarization across a variety of domains. Use this model through the Artifex library: install Artifex with…
Solo GRPO and Tandem RLVR policies from three single-seed experiments. Each Tandem policy is the trained senior; its frozen junior is not included. The reproduction checkpoints are selected by validation pass@4; the others are final step-200 checkpoints. Validation uses each arm's own rollout protocol, so GRPO and Tandem scores are not directly comparable. Base revisions, selection evidence and SHA-256 hashes are in checkpointmanifest.json. The Qwen3.5 checkpoints keep the Qwen35ForConditionalGeneration architecture (text-only training) and are stored in FP32; dtype="auto" loads BF16.
tanpo-deals (Deal Cracker) is a compact B2B deal-closing and distributor/channel specialist for practical commercial work—not generic chatbot chatter. This repository contains the merged Transformers fine-tune, ready to load with transformers or Unsloth. Official DarkLab evaluation on the same 20-task deals rubric and decoding setup. tanpo-deals beats the base by +7.3 percentage points. BEATSBASE: YES Automated rubric results are directional; human judgment remains important for consequential commercial decisions. - GGUF for LM Studio / llama.cpp: d4rkninja/tanpo-deals-GGUF — recommend Q4KM Focused on B2B sales, deal closing, wholesale, distributors, channel partners, negotiation, and…
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.