What are the hardware requirements for Gemma 2 2B Stheno Filtered?

Gemma 2 2B Stheno Filtered 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 Stheno Filtered and how large is it?

Gemma 2 2B Stheno Filtered is developed by SaisExperiments, 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 Stheno Filtered?

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

Gemma 2 2B Stheno Filtered by SaisExperiments — VRAM 5.2GB, 8K context

Name: Gemma 2 2B Stheno Filtered
Author: SaisExperiments

Gemma 2 2B Stheno Filtered is an open-source language model by SaisExperiments. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, License: gemma, LLM Explorer Score: 0.18.

Base model:finetune:google/gem... Base model:google/gemma-2-2b-i... Dataset:anthracite-org/stheno-... Gemma2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/SaisExperiments/Gemma-2-2B-Stheno-Filtered

Gemma 2 2B Stheno Filtered Parameters and Internals

Training Details

Data Sources:

anthracite-org/stheno-filtered-v1.1, google/gemma-2-2b-it

Data Volume:

76.6M tokens

Context Length:

1024

Training Time:

14 hours

LLM Name	Gemma 2 2B Stheno Filtered
Repository 🤗	https://huggingface.co/SaisExperiments/Gemma-2-2B-Stheno-Filtered
Base Model(s)	Gemma 2 2B It google/gemma-2-2b-it
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-07-24
Maintainer	SaisExperiments
Model Type	gemma2
Model Files	5.2 GB
Model Architecture	Gemma2ForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.44.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Gemma 2 2B Stheno Filtered 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 SaisExperiments/Gemma-2-2B-Stheno-Filtered.