What are the hardware requirements for Gemma 3 4B Mllmu Ft?

Gemma 3 4B Mllmu Ft requires approximately 8.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 3 4B Mllmu Ft and how large is it?

Gemma 3 4B Mllmu Ft is developed by Lordvarun23, a model with 4b 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 3 4B Mllmu Ft?

Gemma 3 4B Mllmu Ft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 3 4B Mllmu Ft by Lordvarun23 — VRAM 8.6GB

Name: Gemma 3 4B Mllmu Ft
Author: Lordvarun23

Gemma 3 4B Mllmu Ft is an open-source language model by Lordvarun23. Features: 4b LLM, VRAM: 8.6GB, License: gemma.

Base model:finetune:google/gem... Base model:google/gemma-3-4b-i... Conversational Dataset:mllmmu/mllmu-bench En Endpoints compatible Gemma3 Image-text-to-text Machine-unlearning Mllmu-bench Multimodal Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Lordvarun23/gemma-3-4b-mllmu-ft

Gemma 3 4B Mllmu Ft Parameters and Internals

LLM Name	Gemma 3 4B Mllmu Ft
Repository 🤗	https://huggingface.co/Lordvarun23/gemma-3-4b-mllmu-ft
Base Model(s)	Gemma 3 4B It google/gemma-3-4b-it
Model Size	4b
Required VRAM	8.6 GB
Updated	2026-11-06
Maintainer	Lordvarun23
Model Type	gemma3
Model Files	8.6 GB
Supported Languages	en
Model Architecture	Gemma3ForConditionalGeneration
License	gemma
Transformers Version	5.20.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 3 4B Mllmu Ft 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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Medgemma 4B NPU2	128K / GB	70	0
Medgemma 4B It Abliterated	128K / 6.3 GB	13	6
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Note: green Score (e.g. "73.2") means that the model is better than Lordvarun23/gemma-3-4b-mllmu-ft.