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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
Research checkpoints of sub-100M-parameter English language models, GPT-style decoders (nanoGPT lineage) trained for the The repository holds a controlled scaling study (tokens, width, optimizer) and a set of data-attribution experiments on the 64M model (paired continued-training runs that differ only in data). Every run folder holds plain PyTorch checkpoints (ckpt.pt or ckpt k.pt, where N is the training step in thousands). Folders with a README.md (all runs since 2026-09-25) contain the full specification: data pool and its hash, trainer version, seed, steps and tokens. "Result" is the checkpoint that the reported numbers come from; it is the planned last step (for the final runs, the…
LoRA adapters from one experiment: does adding "good moments" to chat training help a model's welfare, beyond any friendly chat? Chat training data is nearly all work: a user asks for something and the assistant does it. Good moments are the other parts of a real conversation, like being thanked, getting to be curious, or sharing someone's good news. Here each model is trained from its pretrained checkpoint (no vendor post-training), with the same LoRA chat-SFT recipe in every arm. Only the training data differs. moments in the family's own style). generation-based evaluation of a Qwen arm (self-report in interviews, guardrails, benchmarks, anything sampled) is unreliable. Readouts of…
Updated 2026-10-03. This model ranks eight permitted Gooo body paths built from three binary decisions. The project explores a language and a small model route through it, and the compiler checks the assembled body and generates Go. A Go experiment runner then builds and executes the resulting program. Like a workshop, the plan describes what the parts mean and how they may fit. The model helps choose the next assembly; observed test failures guide another attempt. A receipt connects the original intention to the choices and result. Gooo 선언을 설계도, 작은 모델을 조립 순서를 고르는 장치로 생각하면 됩니다. 개발은 Laya의 구조화된 선택 실험에서 출발해, Gooo 자료로 새로 학습한 새 모델을 arm64와 Linux에서 대조한 결과, 첫 선택은 같았고 후순위까지 포함한 일치했습니다. 새 계약은 SDK…
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.