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 2026-09-20 · How the library is built
3,247 models, sorted by most downloaded.
Nebium-Small is a 117-million-parameter causal Transformer trained for autoregressive next-chess-move prediction over Lichess UCI move sequences. - Rotary Position Embeddings (RoPE) on attention query and key projections ($\theta = 10000$) - SwiGLU feed-forward transformation - RMSNorm pre-normalization - Causal mask with padding token masking - Byte-Pair Encoding (BPE) tokenizer trained on UCI move plies $$L(N, D) = 1.69 + \frac{406.4}{N^{0.34}} + \frac{410.7}{D^{0.28}}$$ MIT License.
Quantized and FP16 GGUF format binaries for Nebium-Small (117M parameters). Designed for low-latency CPU and GPU execution with llama.cpp and Ollama. MIT License.
This repository contains a working research note about Multimodal Generation. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they…
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.
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.
Submission withdrawn September 19, 2026 (UTC). Open ASR PR #221 is closed. The leaderboard submission has been withdrawn; the checkpoint, code, research results, and exposure disclosures remain available as historical research artifacts. This repository preserves the original R15-0100 Orukeet checkpoint originally proposed as an earlier checkpoint for Open ASR submission #221. The artifact is unchanged from the archived September 6, 2026 model. It has a FastConformer encoder and TDT decoder derived from NVIDIA Parakeet TDT 0.6B v3, with multilingual/accent adaptation and fitted temporal Gabor kernels materialized in the native weights. The NeMo file is orukeet-r15-0100.nemo (2,509,342,720…
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.