What are the hardware requirements for Gemma 2B Data Std?

Gemma 2B Data Std requires approximately 5.1 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 2B Data Std and how large is it?

Gemma 2B Data Std is developed by jylee420, a model with 2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 2B Data Std perform on standard benchmarks?

Gemma 2B Data Std has the following published scores: MMLU 35.82. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 2B Data Std by jylee420 — VRAM 5.1GB, 8K context

Name: Gemma 2B Data Std
Author: jylee420

Gemma 2B Data Std is an open-source language model by jylee420. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, License: other, LLM Explorer Score: 0.16, Arc: 37.5, HellaSwag: 32.5, MMLU: 35.8, GSM8K: 0.2.

Arxiv:1910.09700 Endpoints compatible Gemma Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/jylee420/gemma-2b-data-std

Gemma 2B Data Std Parameters and Internals

Supported Languages

Korean (trained), English (trained)

Training Details

Data Volume:

6 million tokens

LLM Name	Gemma 2B Data Std
Repository 🤗	https://huggingface.co/jylee420/gemma-2b-data-std
Model Size	2b
Required VRAM	5.1 GB
Updated	2026-08-07
Maintainer	jylee420
Model Type	gemma
Model Files	5.0 GB: 1-of-2 0.1 GB: 2-of-2
Model Architecture	GemmaForCausalLM
License	other
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.1
Tokenizer Class	GemmaTokenizer
Padding Token	<eos>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma 2B Data Std 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 jylee420/gemma-2b-data-std.