What are the hardware requirements for Gemma 2B It Finetuned Linkedin?

Gemma 2B It Finetuned Linkedin 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 2B It Finetuned Linkedin and how large is it?

Gemma 2B It Finetuned Linkedin is developed by pavanKumar2004, 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 2B It Finetuned Linkedin?

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

Gemma 2B It Finetuned Linkedin by pavanKumar2004 — VRAM 5.2GB, 8K context

Name: Gemma 2B It Finetuned Linkedin
Author: pavanKumar2004

Gemma 2B It Finetuned Linkedin is an open-source language model by pavanKumar2004. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, Fine-Tuned, LLM Explorer Score: 0.17.

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

Model Card on HF 🤗: https://huggingface.co/pavanKumar2004/gemma-2b-it-finetuned-linkedin

Gemma 2B It Finetuned Linkedin Parameters and Internals

LLM Name	Gemma 2B It Finetuned Linkedin
Repository 🤗	https://huggingface.co/pavanKumar2004/gemma-2b-it-finetuned-linkedin
Model Size	2b
Required VRAM	5.2 GB
Updated	2025-09-23
Maintainer	pavanKumar2004
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	4.49.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Gemma 2B It Finetuned Linkedin capabilities

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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 pavanKumar2004/gemma-2b-it-finetuned-linkedin.