What are the hardware requirements for Gemma 2 2B It Heisenberg?

Gemma 2 2B It Heisenberg requires approximately 5.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

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

Gemma 2 2B It Heisenberg is developed by dhanush-radhakrishna, 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 Heisenberg?

Gemma 2 2B It Heisenberg 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 Heisenberg by dhanush-radhakrishna — VRAM 5.2GB

Name: Gemma 2 2B It Heisenberg
Author: dhanush-radhakrishna

Gemma 2 2B It Heisenberg is an open-source language model by dhanush-radhakrishna. Features: 2b LLM, VRAM: 5.2GB, LLM Explorer Score: 0.17.

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

Model Card on HF 🤗: https://huggingface.co/dhanush-radhakrishna/gemma-2-2b-it-Heisenberg

Gemma 2 2B It Heisenberg Parameters and Internals

LLM Name	Gemma 2 2B It Heisenberg
Repository 🤗	https://huggingface.co/dhanush-radhakrishna/gemma-2-2b-it-Heisenberg
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-07-27
Maintainer	dhanush-radhakrishna
Model Files	2.4 GB 5.0 GB: 1-of-2 0.2 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	down_proj\|up_proj\|gate_proj\|v_proj\|o_proj\|k_proj\|q_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Gemma 2 2B It Heisenberg 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 dhanush-radhakrishna/gemma-2-2b-it-Heisenberg.