What are the hardware requirements for Gemma 7B Bnb 4bit?

Gemma 7B Bnb 4bit requires approximately 17.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 7B Bnb 4bit and how large is it?

Gemma 7B Bnb 4bit is developed by tung2308, a model with 7b 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 7B Bnb 4bit?

Gemma 7B Bnb 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 7B Bnb 4bit by tung2308 — VRAM 17.1GB

Name: Gemma 7B Bnb 4bit
Author: tung2308

Gemma 7B Bnb 4bit is an open-source language model by tung2308. Features: 7b LLM, VRAM: 17.1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Base model:finetune:unsloth/ge... Base model:unsloth/gemma-7b-bn... En Endpoints compatible Gemma Lora Pytorch Quantized Region:us Safetensors Sft Sharded Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/tung2308/gemma-7b-bnb-4bit

Gemma 7B Bnb 4bit Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, gemma, trl, sft

Additional Notes

This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.

Training Details

Methodology:

unsloth, Huggingface's TRL library

Training Time:

2x faster

LLM Name	Gemma 7B Bnb 4bit
Repository 🤗	https://huggingface.co/tung2308/gemma-7b-bnb-4bit
Base Model(s)	Gemma 7B Bnb 4bit unsloth/gemma-7b-bnb-4bit
Model Size	7b
Required VRAM	17.1 GB
Updated	2026-05-17
Maintainer	tung2308
Model Files	0.2 GB 5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.1 GB: 4-of-4
Supported Languages	en
Quantization Type	4bit
Model Architecture	AutoModelForCausalLM
License	apache-2.0
Model Max Length	8192
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	o_proj\|up_proj\|q_proj\|down_proj\|k_proj\|gate_proj\|v_proj
LoRA Alpha	16
LoRA Dropout	0
R Param	16

Rank the Gemma 7B Bnb 4bit 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 tung2308/gemma-7b-bnb-4bit.