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 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
This package runs the Motif-3 LLM of Motif Technologies on one Tenstorrent Blackhole Galaxy (32 Blackhole chips). - 53 layers of Grouped Differential Latent Attention (GDLA). - A 4-stream mHC residual. - 384 routed experts and 1 shared expert, all PolyNorm MLPs, with top-8 routing. - 1 MTP (multi-token-prediction) layer. - default: for all traffic. - mtp (opt-in): greedy requests decode up to 1.9× faster with the model's own MTP layer. - Built on tt-metal / ttnn as a plain vLLM model behind the vllm-tt-plugin. - Mesh (4, 8): attention is 8-way tensor-parallel in 4 groups of 8 user lanes; the MoE is 32-way expert-parallel (12 experts per chip). - Decode runs as captured Metal traces. The MTP…
MSBuddy project, maintained by Shipei Xing. Original database files from msbuddydatav0.2.4 and LightGBM model from msbuddydatav0.3.0. Apache-2.0 is recorded on the basis of the upstream project licence; the release pages do not state separate asset-specific terms. Its application to these files is an inference, not an independently confirmed per-file licence grant. The original licence is retained as db0.2.4-ml0.3.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.
Wan2.1-Fun-1.3B-Inp/ - wav2vec2-base-960h/ - extra/13B-single-v1.pth - extra/14B-multi-v1.pth - extra/configaf2v13B.json - extra/configaf2v14B.json
This model is a fine-tuned version of ALLaM-AI/ALLaM-7B-Instruct-preview. It has been trained using TRL. This model was trained with SFT.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is Horizon-Labs/multilingual-reranker-small in a smaller ONNX file, for running on a CPU. Same model, same tokenizer, same scores to within a small margin. The file is about a third smaller than the original quantized one. We use it in Sno Station to rerank memories for coding agents. The token embeddings, the attention projections and the first MLP projection are stored in 8 bits. The second MLP projection stays in float. Its inputs carry a few very large values, and 8 bits move the scores too much there. Weights are unsigned 8-bit, per channel. Signed weights overflow on AVX2 CPUs without VNNI and flatten every score. quantize.py rebuilds the file from the original onnx/model.onnx at…
Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score…
Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score…
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 notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they…
This repository contains a working research note about Multimodal Reasoning. 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 VQAv2, GQA, and NLVR2 - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions…
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.