What are the hardware requirements for Vi Humanizer Gemma 4 E2b?

Vi Humanizer Gemma 4 E2b requires approximately 3.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vi Humanizer Gemma 4 E2b and how large is it?

Vi Humanizer Gemma 4 E2b is developed by contextboxai, 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 Vi Humanizer Gemma 4 E2b?

Vi Humanizer Gemma 4 E2b is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Vi Humanizer Gemma 4 E2b by contextboxai — VRAM 3.4GB

Name: Vi Humanizer Gemma 4 E2b
Author: contextboxai

Vi Humanizer Gemma 4 E2b is an open-source language model by contextboxai. Features: 5.1b LLM, VRAM: 3.4GB, License: gemma, Quantized.

Base model:google/gemma-4-e2b-... Base model:quantized:google/ge... Conversational En Endpoints compatible Gemma4 Gguf Humanizer Image-text-to-text Llama.cpp Q4 Quantized Region:us Safetensors Vi Vietnamese

Model Card on HF 🤗: https://huggingface.co/contextboxai/vi-humanizer-gemma-4-e2b

Vi Humanizer Gemma 4 E2b Parameters and Internals

LLM Name	Vi Humanizer Gemma 4 E2b
Repository 🤗	https://huggingface.co/contextboxai/vi-humanizer-gemma-4-e2b
Base Model(s)	Gemma 4 E2B It google/gemma-4-E2B-it
Model Size	5.1b
Required VRAM	3.4 GB
Updated	2026-10-22
Maintainer	contextboxai
Model Type	gemma4
Model Files	0.0 GB 10.2 GB 3.4 GB 3.8 GB 5.0 GB 9.3 GB
Supported Languages	vi en
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|q4_k
Model Architecture	Gemma4ForConditionalGeneration
License	gemma
Transformers Version	5.17.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Vi Humanizer Gemma 4 E2b 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

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Note: green Score (e.g. "73.2") means that the model is better than contextboxai/vi-humanizer-gemma-4-e2b.