What are the hardware requirements for Chinse?

Chinse requires approximately 10.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Chinse and how large is it?

Chinse is developed by jasort, a model with 5.1b parameters. 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 Chinse?

Chinse is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Chinse by jasort — VRAM 10.3GB

Name: Chinse
Author: jasort

Chinse is an open-source language model by jasort. Features: 5.1b LLM, VRAM: 10.3GB, License: apache-2.0, LLM Explorer Score: 0.29.

Base model:finetune:unsloth/ge... Base model:unsloth/gemma-4-e2b... Conversational En Endpoints compatible Gemma4 Image-text-to-text Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/jasort/chinse

Chinse Parameters and Internals

LLM Name	Chinse
Repository 🤗	https://huggingface.co/jasort/chinse
Base Model(s)	Gemma 4 E2B It unsloth/gemma-4-E2B-it
Model Size	5.1b
Required VRAM	10.3 GB
Updated	2026-08-30
Maintainer	jasort
Model Type	gemma4
Model Files	1.4 GB: 1-of-5 4.7 GB: 2-of-5 1.6 GB: 3-of-5 1.6 GB: 4-of-5 1.0 GB: 5-of-5
Supported Languages	en
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Model Max Length	131072
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Torch Data Type	bfloat16

Rank the Chinse 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 Chinse

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 4 E2B It	0K / 10.2 GB	3222438	654
Gemma 4 E2B	0K / 10.2 GB	79938	425
Gemma 4 E2B It	0K / 10.2 GB	69653	13
Gemma 4 E2B	0K / 10.2 GB	3151	0
KoGemma E2B	0K / 10.2 GB	197	0
Gemma SEA LION V4.5 E2B IT	0K / 10.2 GB	2783	12
Himalaya Gemma 4 E2b It	0K / 10.2 GB	709	14
Gemma 4 E2B	0K / 10.2 GB	2537	9
Rostam R1 Stage13	0K / 10.2 GB	27	0
Rostam R1 Stage12 Part2	0K / 10.2 GB	20	0

Note: green Score (e.g. "73.2") means that the model is better than jasort/chinse.