What are the hardware requirements for Gemma 2 2B It Xaas Qa?

Gemma 2 2B It Xaas Qa requires approximately 5.2 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 2 2B It Xaas Qa and how large is it?

Gemma 2 2B It Xaas Qa is developed by lablup, 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 2 2B It Xaas Qa?

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

Gemma 2 2B It Xaas Qa by lablup — VRAM 5.2GB, 8K context

Name: Gemma 2 2B It Xaas Qa
Author: lablup

Gemma 2 2B It Xaas Qa is an open-source language model by lablup. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, License: gemma, Merged, LLM Explorer Score: 0.24.

Merged Model Base model:adapter:lablup/gemm... Base model:lablup/gemma-2-2b-i... Conversational Customs En Endpoints compatible Gemma2 Hs-code Incoterms Instruct Ko Korean Lora Peft Region:us Safetensors Sharded Tensorflow Trade

Model Card on HF 🤗: https://huggingface.co/lablup/gemma-2-2b-it-xaas-qa

Gemma 2 2B It Xaas Qa Parameters and Internals

LLM Name	Gemma 2 2B It Xaas Qa
Repository 🤗	https://huggingface.co/lablup/gemma-2-2b-it-xaas-qa
Base Model(s)	lablup/gemma-2-2b-it-xaas-cpt lablup/gemma-2-2b-it-xaas-cpt
Merged Model	Yes
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-06-27
Maintainer	lablup
Model Type	gemma2
Model Files	5.0 GB: 1-of-2 0.2 GB: 2-of-2
Supported Languages	ko en
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.43.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma 2 2B It Xaas Qa 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 lablup/gemma-2-2b-it-xaas-qa.