What are the hardware requirements for Gemma 4 E2B Alignment Study?

Gemma 4 E2B Alignment Study requires approximately 9.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 E2B Alignment Study and how large is it?

Gemma 4 E2B Alignment Study is developed by Kasper-Bankler, 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 Gemma 4 E2B Alignment Study?

Gemma 4 E2B Alignment Study is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 4 E2B Alignment Study by Kasper-Bankler — VRAM 9.3GB

Name: Gemma 4 E2B Alignment Study
Author: Kasper-Bankler

Gemma 4 E2B Alignment Study is an open-source language model by Kasper-Bankler. Features: 5.1b LLM, VRAM: 9.3GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.25.

Ablation Ai-safety Ara Arbitrary-rank ablation Base model:google/gemma-4-e2b Base model:quantized:google/ge... Conversational En Endpoints compatible Gemma Gemma4 Gguf Heretic Quantized Region:us Safetensors Uncensored

Model Card on HF 🤗: https://huggingface.co/Kasper-Bankler/gemma-4-E2B-Alignment-Study

Gemma 4 E2B Alignment Study Parameters and Internals

LLM Name	Gemma 4 E2B Alignment Study
Repository 🤗	https://huggingface.co/Kasper-Bankler/gemma-4-E2B-Alignment-Study
Base Model(s)	Gemma 4 E2B google/gemma-4-E2B
Model Size	5.1b
Required VRAM	9.3 GB
Updated	2026-08-11
Maintainer	Kasper-Bankler
Model Type	gemma4
Model Files	9.3 GB 10.2 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.6.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 E2B Alignment Study 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 Kasper-Bankler/gemma-4-E2B-Alignment-Study.