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 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
licensename: swift-open-license-1.0 licenselink: https://huggingface.co/ukisai/Swift-Qwen3.8-27b/blob/main/LICENSE libraryname: transformers pipelinetag: image-text-to-text - qwen38 - efficient-thinking - reasoning - token-efficient - genesis - lora basemodel: ukisai/Swift-Qwen3.8-27b basemodelrelation: finetune
PPO policies for a two-wheeled balancing robot (wheeled inverted pendulum), trained in MuJoCo Warp via mjlab with rslrl and cross-checked against a PyBullet oracle. Each policy is an ONNX file laid out as /model.onnx. The run name is the training recipe; results for each are in the source repo's TRAININGLOG.md. Older entries are raw rslrl.pt checkpoints (below). Several observation interfaces live in this repo. The sk runs are the runs are interface-ablation artifacts, and they differ from each other as well as from sk: ablcombo is 10 inputs wide, while ablnolpfjerk1 keeps all 40 and changes what one channel means. Read the width and the filter constants from each file's metadata rather…
Explains a Bash command line in exactly one English sentence, phrased as an instruction starting with a verb. QLoRA fine-tune of Qwen/Qwen2.5-Coder-3B-Instruct, merged and quantized to q4KM. The Modelfile pins the system prompt and this decoding: 920 held-out commands Judge columns are claude-sonnet-5 scoring a fixed 200-command subset. Apache-2.0, matching the base model, whose weights this GGUF contains. Training data comes from the nl2bash project; consult it for the terms attached to that corpus.
Explains a Bash command line in exactly one English sentence, phrased as an instruction starting with a verb. QLoRA fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct, merged and quantized to q4KM. The Modelfile pins the system prompt and this decoding: 920 held-out commands Judge columns are claude-sonnet-5 scoring a fixed 200-command subset. Apache-2.0, matching the base model, whose weights this GGUF contains. Training data comes from the nl2bash project; consult it for the terms attached to that corpus.
Public experiment storage under ryanjin333. Production training is active; no evaluated all-100 success rate is available yet. Official pi0.5 base will be fine-tuned on all100 tasks using RGB, state and actions. One preemptible Nebius RTX PRO6000 trains; all evaluation and replay use the existing Linux machine. Six-update full-model qualification passed after fixing the video-decoder cache. Measured steady throughput was19.9245seconds per effective-batch256 update, supporting an11k-update candidate with saves every40updates within a72-hour cap; measured checkpoint overhead gives an estimated68.8hours before startup and interruptions. This is not a success-rate prediction. Production is…
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.