What are the hardware requirements for Gemma 2 2B It Fine Review?

Gemma 2 2B It Fine Review 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 Fine Review and how large is it?

Gemma 2 2B It Fine Review is developed by najeebtpni001, 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 Fine Review?

Gemma 2 2B It Fine Review 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 Fine Review by najeebtpni001 — VRAM 5.2GB, 8K context

Name: Gemma 2 2B It Fine Review
Author: najeebtpni001

Gemma 2 2B It Fine Review is an open-source language model by najeebtpni001. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, LLM Explorer Score: 0.17.

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

Model Card on HF 🤗: https://huggingface.co/najeebtpni001/Gemma-2-2b-it-fine-review

Gemma 2 2B It Fine Review Parameters and Internals

LLM Name	Gemma 2 2B It Fine Review
Repository 🤗	https://huggingface.co/najeebtpni001/Gemma-2-2b-it-fine-review
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-07-26
Maintainer	najeebtpni001
Model Type	gemma2
Model Files	5.0 GB: 1-of-2 0.2 GB: 2-of-2
Model Architecture	Gemma2ForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.50.0
Tokenizer Class	GemmaTokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	256002
Torch Data Type	float16

Rank the Gemma 2 2B It Fine Review 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 najeebtpni001/Gemma-2-2b-it-fine-review.