What are the hardware requirements for Gemma E4b It ModeltrainingCFT SFT?

Gemma E4b It ModeltrainingCFT SFT requires approximately 16 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma E4b It ModeltrainingCFT SFT and how large is it?

Gemma E4b It ModeltrainingCFT SFT is developed by ajujohn8, a model with 8b 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 E4b It ModeltrainingCFT SFT?

Gemma E4b It ModeltrainingCFT SFT is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma E4b It ModeltrainingCFT SFT by ajujohn8 — VRAM 16GB

Name: Gemma E4b It ModeltrainingCFT SFT
Author: ajujohn8

Gemma E4b It ModeltrainingCFT SFT is an open-source language model by ajujohn8. Features: 8b LLM, VRAM: 16GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.25.

4bit Base model:ajujohn8/gemma-4-e4... Base model:finetune:ajujohn8/g... Conversational En Endpoints compatible Gemma4 Image-text-to-text Quantized Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/ajujohn8/gemma-e4b-it-modeltrainingCFT-SFT

Gemma E4b It ModeltrainingCFT SFT Parameters and Internals

LLM Name	Gemma E4b It ModeltrainingCFT SFT
Repository 🤗	https://huggingface.co/ajujohn8/gemma-e4b-it-modeltrainingCFT-SFT
Model Name	ajujohn8/gemma-4-e4b-it-unsloth-bnb-4bit-aj
Base Model(s)	...a 4 E4b It Unsloth Bnb 4bit Aj ajujohn8/gemma-4-e4b-it-unsloth-bnb-4bit-aj
Model Size	8b
Required VRAM	16 GB
Updated	2026-08-09
Maintainer	ajujohn8
Model Type	gemma4
Model Files	16.0 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Model Max Length	131072
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Torch Data Type	bfloat16

Rank the Gemma E4b It ModeltrainingCFT SFT capabilities

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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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...e 4.6 Opus Reasoning Distilled	0K / 1 GB	1539	19
Gemma 4 E4B It OBLITERATED	0K / 16 GB	58062	739
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Note: green Score (e.g. "73.2") means that the model is better than ajujohn8/gemma-e4b-it-modeltrainingCFT-SFT.