What are the hardware requirements for Gemma 2 Tiny Random?

Gemma 2 Tiny Random requires 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 Tiny Random and how large is it?

Gemma 2 Tiny Random is developed by yujiepan, a model with 27b 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 Tiny Random?

Gemma 2 Tiny Random is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 2 Tiny Random by yujiepan — 8K context

Name: Gemma 2 Tiny Random
Author: yujiepan

Gemma 2 Tiny Random is an open-source language model by yujiepan. Features: 27b LLM, Context: 8K, LLM Explorer Score: 0.14.

Base model:finetune:google/gem... Base model:google/gemma-2-27b-... Conversational Endpoints compatible Gemma2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/yujiepan/gemma-2-tiny-random

Gemma 2 Tiny Random Parameters and Internals

Model Type

text generation

Additional Notes

This model is randomly initialized using a modified configuration of the original Google GEMMA model, with a smaller scale in parameters such as hidden size and number of layers.

LLM Name	Gemma 2 Tiny Random
Repository 🤗	https://huggingface.co/yujiepan/gemma-2-tiny-random
Base Model(s)	Gemma 2 27B It google/gemma-2-27b-it
Model Size	27b
Required VRAM	0 GB
Updated	2026-08-07
Maintainer	yujiepan
Model Type	gemma2
Model Files	0.0 GB
Model Architecture	Gemma2ForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.57.1
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Gemma 2 Tiny Random 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 yujiepan/gemma-2-tiny-random.