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 2026-10-18 · How the library is built
11,122 models, sorted by most downloaded.
Base model for the Lite tier of Saathi, a private offline journaling companion. - Converted with llama.cpp (b11192) from google/gemma-3-1b-it-qat-q40-unquantized. Google's own q40 GGUF has slidingwindow 1024 and no freqbaseswa (the config says 512 / 10000), which breaks prompts longer than 512 tokens; this file fixes that. sha256 fe611e51b2e038f26bf77a1eb29bac385cbf1b1256629b6b255d444d2e8f590b - Perplexity (60 dialogue pairs, nctx 256): this file 49.60, Google's q40 49.34. Gemma is provided under and subject to the Gemma Terms of Use found at https://ai.google.dev/gemma/terms
not-for-all-audiences
Meta SAM 3 (Segment Anything Model 3), exported to ONNX (FP16) for a C++ ONNX Runtime pipeline: a vision encoder, a text encoder and a decoder with a dynamic prompt-count batch (a single file accepts any number of text prompts at runtime, no re-export needed). The model is derived from Meta's SAM 3 and is provided under the SAM License (see LICENSE in this repository). Use, reproduction and redistribution are subject to that agreement; the license text must be kept with any redistribution.
The tool picker inside Turing 5.0. Samantha reads one sentence and picks one of her tools on your Mac, with the words to run it on, as one JSON line: {"tool": "openapp", "arg": "Safari"}. If the sentence is not a command, she says so: {"tool": null, "arg": ""}. Qwen2.5-1.5B-Instruct, fine-tuned on Kaggle (SFT, then a gentle DPO stage), quantized to 4 bits for Apple Silicon with mlx-lm. About 850 MB. She never runs a pick on her own say-so. Every pick goes through Turing's guard, which checks that the argument was copied from the sentence and that the sentence is really about that tool, and anything that writes asks first. Measured on sentences she never trained on: under 10 wrong picks past…
samai-27b（Qwen/Qwen3.8-27B 手术版：PonderNet 停机位点 L35/39/43/47/51/55/59/63 + dMoE 8 专家 + MTP）经 QLoRA SFT 后，按 mixbit 静态化管线产出的 llama.cpp 混合精度权重。 等价稠密模型 ≙ Qwen35ForCausalLM（48 linearattention + 16 fullattention，hidden 5120），可直接用上游 llama.cpp 载入。 - Token embedding / output：Q6K（--token-embedding-type Q6K） OpenAI 兼容端点 /v1/chat/completions。 - 基座：tchbcb/samai-27b（含 SFT adapter / 训练日志 / 数据） - 配方参考：tchbcb/samai-4b-mixbit
A Santali speech-synthesis model for text written in the Ol Chiki script. The model was trained using the Piper VITS training pipeline and exported to ONNX for offline inference and deployment on supported platforms. The exported ONNX inference model. This is the primary deployment artifact. The corresponding Piper model configuration. Training configuration containing the model and text-symbol configuration. The custom text-symbol inventory used for Santali written in Ol Chiki. Training metadata for the speech samples used in this training run. Machine-readable metadata describing the model, training run, dataset, and validation results. The PyTorch Lightning checkpoint is retained…
by AGK FIRE INC A Qwen3-8B fine-tune that tutors for the SAT (math + reading/writing), packaged as a Q4KM GGUF for local inference with llama.cpp, Ollama, LM Studio, and friends. Works through SAT questions step by step and finishes with a clear Answer: X line. Trained on SAT-style math and reading/writing questions with step-by-step solutions. Held-out eval: 160 questions, identical prompt and decoding for both (greedy, 400 tokens, thinking off), strict scoring = unparseable counted wrong. the adapter overfit toward math and wants more RW data before the teacher is frozen The tuned adapter was chosen as the quantization target. Q4KM accuracy on the same suite: run the "Measure the…
These are the checkpoints needed to continue training SAWT V4. If you only want to restore audio, use Quran-Lab/sawt-v4 instead: it carries the trimmed inference weights and the client code. The released generator.pt, anchor.pt and vae48dacvaed2.pt in the model repository are derived from these by stripping what inference does not need.
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).
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.