What are the hardware requirements for Gemma 2B JapaneseQA Instruct?

Gemma 2B JapaneseQA Instruct requires approximately 2.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 2B JapaneseQA Instruct and how large is it?

Gemma 2B JapaneseQA Instruct is developed by kanxxyc, a model with 2b 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 2B JapaneseQA Instruct?

Gemma 2B JapaneseQA Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 2B JapaneseQA Instruct by kanxxyc — VRAM 2.1GB

Name: Gemma 2B JapaneseQA Instruct
Author: kanxxyc

Gemma 2B JapaneseQA Instruct is an open-source language model by kanxxyc. Features: 2b LLM, VRAM: 2.1GB, License: other, Instruction-Based, LLM Explorer Score: 0.11.

Adapter Base model:adapter:google/gemm... Base model:google/gemma-2b Dataset:generator Finetuned Generated from trainer Instruct Lora Peft Region:us Safetensors Sft Tensorboard Trl

Model Card on HF 🤗: https://huggingface.co/kanxxyc/gemma-2b-japaneseQA-instruct

Gemma 2B JapaneseQA Instruct Parameters and Internals

Additional Notes

More information is needed for model description, intended uses, limitations, and training results.

Training Details

Data Sources:

generator

LLM Name	Gemma 2B JapaneseQA Instruct
Repository 🤗	https://huggingface.co/kanxxyc/gemma-2b-japaneseQA-instruct
Base Model(s)	Gemma 2B google/gemma-2b
Model Size	2b
Required VRAM	2.1 GB
Updated	2026-08-06
Maintainer	kanxxyc
Instruction-Based	Yes
Model Files	2.1 GB 0.0 GB
Model Architecture	Adapter
License	other
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<\|im_end\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|gate_proj\|v_proj\|q_proj\|down_proj\|o_proj\|up_proj
LoRA Alpha	8
LoRA Dropout	0.05
R Param	6

Rank the Gemma 2B JapaneseQA Instruct 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

Best Alternatives to Gemma 2B JapaneseQA Instruct

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Kokolok 2.0 Vision	0K / 0 GB	38	1
Gemma 2B It Fr	0K / 0 GB	7	2

Note: green Score (e.g. "73.2") means that the model is better than kanxxyc/gemma-2b-japaneseQA-instruct.