What are the hardware requirements for Gemma 2 2B It Alpaca Cleaned SFT 1024?

Gemma 2 2B It Alpaca Cleaned SFT 1024 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 Alpaca Cleaned SFT 1024 and how large is it?

Gemma 2 2B It Alpaca Cleaned SFT 1024 is developed by vectorzhou, 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 Alpaca Cleaned SFT 1024?

Gemma 2 2B It Alpaca Cleaned SFT 1024 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 Alpaca Cleaned SFT 1024 by vectorzhou — VRAM 5.2GB, 8K context

Name: Gemma 2 2B It Alpaca Cleaned SFT 1024
Author: vectorzhou

Gemma 2 2B It Alpaca Cleaned SFT 1024 is an open-source language model by vectorzhou. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, LLM Explorer Score: 0.2.

Arxiv:1910.09700 Conversational Endpoints compatible Gemma2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024

Gemma 2 2B It Alpaca Cleaned SFT 1024 Parameters and Internals

LLM Name	Gemma 2 2B It Alpaca Cleaned SFT 1024
Repository 🤗	https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-06-07
Maintainer	vectorzhou
Model Type	gemma2
Model Files	1.2 GB: 1-of-2 4.0 GB: 2-of-2
Model Architecture	Gemma2ForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.56.1
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000

Rank the Gemma 2 2B It Alpaca Cleaned SFT 1024 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 vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024.