What are the hardware requirements for Gemma 2 2B It Game Recommendation?

Gemma 2 2B It Game Recommendation requires approximately 5.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 2 2B It Game Recommendation and how large is it?

Gemma 2 2B It Game Recommendation is developed by dpwls003, 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 Game Recommendation?

Gemma 2 2B It Game Recommendation 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 Game Recommendation by dpwls003 — VRAM 5.1GB

Name: Gemma 2 2B It Game Recommendation
Author: dpwls003

Gemma 2 2B It Game Recommendation is an open-source language model by dpwls003. Features: 2b LLM, VRAM: 5.1GB, LLM Explorer Score: 0.14.

Arxiv:1910.09700 Base model:finetune:google/gem... Base model:google/gemma-2-2b-i... Conversational Endpoints compatible Gemma Lora Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/dpwls003/Gemma-2-2b-it-game-recommendation

Gemma 2 2B It Game Recommendation Parameters and Internals

LLM Name	Gemma 2 2B It Game Recommendation
Repository 🤗	https://huggingface.co/dpwls003/Gemma-2-2b-it-game-recommendation
Base Model(s)	Gemma 2 2B It google/gemma-2-2b-it
Model Size	2b
Required VRAM	5.1 GB
Updated	2026-07-23
Maintainer	dpwls003
Model Files	2.2 GB 5.0 GB: 1-of-2 0.1 GB: 2-of-2
Model Architecture	AutoModelForCausalLM
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<\|im_end\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	up_proj\|v_proj\|q_proj\|down_proj\|gate_proj\|o_proj\|k_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Gemma 2 2B It Game Recommendation 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 dpwls003/Gemma-2-2b-it-game-recommendation.