What are the hardware requirements for Gemma4 E2B FACEBOOK PDEFAULT E2 BF16?

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 requires approximately 10.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 and how large is it?

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 is developed by taharmasmaliyev07, a model with 5.1b 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 Gemma4 E2B FACEBOOK PDEFAULT E2 BF16?

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 by taharmasmaliyev07 — VRAM 10.2GB

Name: Gemma4 E2B FACEBOOK PDEFAULT E2 BF16
Author: taharmasmaliyev07

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 is an open-source language model by taharmasmaliyev07. Features: 5.1b LLM, VRAM: 10.2GB, License: apache-2.0.

Base model:finetune:taharmasma... Base model:taharmasmaliyev07/g... Conversational En Endpoints compatible Gemma4 Image-text-to-text Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/taharmasmaliyev07/Gemma4-E2B-FACEBOOK-PDEFAULT-E2-BF16

Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 Parameters and Internals

LLM Name	Gemma4 E2B FACEBOOK PDEFAULT E2 BF16
Repository 🤗	https://huggingface.co/taharmasmaliyev07/Gemma4-E2B-FACEBOOK-PDEFAULT-E2-BF16
Base Model(s)	... E2B FACEBOOK PDEFAULT E2 BF16 taharmasmaliyev07/Gemma4-E2B-FACEBOOK-PDEFAULT-E2-BF16
Model Size	5.1b
Required VRAM	10.2 GB
Updated	2026-11-14
Maintainer	taharmasmaliyev07
Model Type	gemma4
Model Files	10.2 GB
Supported Languages	en
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.17.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Torch Data Type	bfloat16

Rank the Gemma4 E2B FACEBOOK PDEFAULT E2 BF16 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 taharmasmaliyev07/Gemma4-E2B-FACEBOOK-PDEFAULT-E2-BF16.