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.
Not a stock voice. A voice. A Chinese-exclusive TTS voice model by Zixi Li. VITS2 lineage (forked from MeloTTS), 51.43M parameters, Chinese-only, no BERT. - Small — 51M params is light enough for local inference, CPU included. No cloud, no API key, no GPU bill. - Chinese-exclusive — the whole stack serves one language: 77 symbols, 6 tones, zero language-routing overhead. - One real person's voice — a single real human's voice, so the output keeps one person's timbre, rhythm and breath. ▶ finalshowcase.wav — one take, default inference parameters (sdpratio=0.2, noisescale=0.6, noisescalew=0.8), text from Zixi Li's own fiction. Weights only — training data is not part of this release. The…
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).
Status: experimental test completed; continuous background mode is disabled. The test did not change the serving checkpoint, app, or model weight files in this repository. This update documents the experiment only. A separate controller completed two successive coach → learner cycles without chat input, then stopped at its configured limit. It supplied a planning request and a follow-up, retaining the actual first coach answer as conversation history. The second lesson drafted with the checkpoint learned from the first. The student used the existing Map5/SEE memory and linear reranker, plus the frozen FP32 GPT2-medium chooser. The external coach was Qwen/Qwen2.5-32B-Instruct, revision…
A LoRA adapter that turns openbmb/MiniCPM-V-4.7-1B (open-weight ~1.5B VLM, Apache-2.0) into a focused document-OCR model for Devanagari (Hindi) + English text, including the conjuncts and mixed-script layouts where the base model is weakest. pure-Devanagari, mixed Hindi+English+digits, conjunct-heavy vocabulary (क्षेत्र, ज्ञान, त्रिभुज, …) gradient checkpointing, NaViT variable-shape slicing (4× downsample, ≤36 slices) Measured with eval/runeval.py (NF4, seed 0) + eval/metrics.py (NFC-normalized, whitespace-stripped Levenshtein CER). Per-bucket mean CER on the 50-image set (base → LoRA): The 50-image set is genuinely out-of-distribution (real fonts/layouts vs synthetic training), so the…
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.