Local continue-train on glasseye RTX 5070 from openmythos-security-v0.1 (100 steps). Authorized research / detection-oriented use. Note: checkpoint remap had partial key mismatches; treat as experimental refresh.
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.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
Pre-converted OpenVINO GenAI models, ready to use with ovtool. Every llm/vlm model ships the full five-variant ladder: int4-asym-g128 / int4-sym-g128 / int4-awq-g128 / int8 / fp16. - Image input for Qwen3-VL / Qwen3.5 hits a shape-compatibility issue in this GenAI release (fixed on master, pending release); text-only mode works fine - NPU supports only symmetric INT4 models, and requires a fixed prompt/response budget at pipeline compile time (handled automatically by ovtool)
Latest local refresh: see opus-4.8-recreation-1b-light-v4 under the same org (glasseye RTX 5070).
Continued light-mode training on glasseye RTX 5070 only (no Modal/HF Jobs). Load with MoE?Expert swap as documented on the baseline card.
Continued light-mode training on glasseye RTX 5070 only (no Modal/HF Jobs). Load with MoE?Expert swap as documented on the baseline card.
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…
Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…
Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…
LoRA fine-tune của π0.5 (pi05base) cho tay máy Yahboom X3Plus (5 khớp + gripper, 20 Hz, 2 camera), nhiệm vụ "pick up the red cube and put it in the bowl". - Chưa kiểm chứng trên robot thật. Mọi số liệu ở trên là loss huấn luyện. - Loss đi ngang từ bước ~4.000. 6.000 bước sau chỉ giảm thêm 36%, trong biên độ nhiễu. - Một nhiệm vụ, một bối cảnh, một bộ camera. Nhiều khả năng hỏng khi đổi vị trí - Camera cổ tay (USB webcam) cho ảnh mờ, nhiều frame gần như trắng khi áp sát mặt bàn. Cần config pi05x3pluslora và lớp LeRobotX3PlusDataConfig tương ứng trong openpi (ánh xạ astrargb → base0rgb, usbcam → leftwrist0rgb, delta mask (5, -1)). yahboomx3plus · joint1–5 tính bằng radian (URDF x3plusarm)…
LeRobot policy checkpoints uploaded by goalgen/uploadhfcheckpoints.sh. Each subfolder contains the deployment-ready pretrainedmodel/ payload (model.safetensors + config.json + pre/postprocessor + trainconfig.json).
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.