What are the hardware requirements for Tsukuyomi Chan Style Bert Vits2 Model?

Tsukuyomi Chan Style Bert Vits2 Model requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tsukuyomi Chan Style Bert Vits2 Model and how large is it?

Tsukuyomi Chan Style Bert Vits2 Model is developed by ayousanz. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Tsukuyomi Chan Style Bert Vits2 Model?

Tsukuyomi Chan Style Bert Vits2 Model is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tsukuyomi Chan Style Bert Vits2 Model by ayousanz — VRAM 0.2GB

Name: Tsukuyomi Chan Style Bert Vits2 Model
Author: ayousanz

Tsukuyomi Chan Style Bert Vits2 Model is an open-source language model by ayousanz. Features: LLM, VRAM: 0.2GB, License: other, LLM Explorer Score: 0.11.

Base model:finetune:litagin/st... Base model:litagin/style-bert-... Endpoints compatible Jp Region:us Stylebertvits2 つくよみちゃん

Model Card on HF 🤗: https://huggingface.co/ayousanz/tsukuyomi-chan-style-bert-vits2-model

Tsukuyomi Chan Style Bert Vits2 Model Parameters and Internals

Model Type

speech synthesis

Training Details

Data Sources:

つくよみちゃんコーパス│声優統計コーパス（JVSコーパス準拠）

LLM Name	Tsukuyomi Chan Style Bert Vits2 Model
Repository 🤗	https://huggingface.co/ayousanz/tsukuyomi-chan-style-bert-vits2-model
Model Name	tsukuyomi-chan
Base Model(s)	litagin/Style-Bert-VITS2-2.0-base-JP-Extra litagin/Style-Bert-VITS2-2.0-base-JP-Extra
Required VRAM	0.2 GB
Updated	2026-07-15
Maintainer	ayousanz
Model Files	0.2 GB 0.2 GB 0.2 GB 0.2 GB 0.2 GB 0.2 GB
Supported Languages	jp
Model Architecture	AutoModel
License	other

Rank the Tsukuyomi Chan Style Bert Vits2 Model capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Tsukuyomi Chan Style Bert Vits2 Model

Best Alternatives	Context / RAM	Downloads	Likes
Distil Longformer Base 4096	4K / 0.4 GB	6	0
Daedalus 1	1K / GB	9	1
Opengpt2 Pytorch Backward	1K / 6 GB	325	1
Tiny Random Detr	1K / 0.2 GB	15	0
Opengpt2 Pytorch Forward	1K / 6 GB	2	1
Finsent Transformer	0.5K / 0.4 GB	1	1
Bert Chinese L 12 H 768 A 12	0.5K / 0.4 GB	9	1
Simbert Chinese Base	0.5K / 0.4 GB	11	0
Simbert Chinese Tiny	0.5K / 0 GB	9	0
Bert Tiny	0.5K / 0 GB	1360936	147

Note: green Score (e.g. "73.2") means that the model is better than ayousanz/tsukuyomi-chan-style-bert-vits2-model.