What are the hardware requirements for Slm Gemma 2B Qa?

Slm Gemma 2B Qa 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 Slm Gemma 2B Qa and how large is it?

Slm Gemma 2B Qa is developed by thesreedath, 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 Slm Gemma 2B Qa?

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

Slm Gemma 2B Qa by thesreedath — VRAM 5.2GB, 8K context

Name: Slm Gemma 2B Qa
Author: thesreedath

Slm Gemma 2B Qa is an open-source language model by thesreedath. Features: 2b LLM, VRAM: 5.2GB, Context: 8K, LLM Explorer Score: 0.25.

Gemma2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/thesreedath/slm-gemma-2b-qa

Slm Gemma 2B Qa Parameters and Internals

LLM Name	Slm Gemma 2B Qa
Repository 🤗	https://huggingface.co/thesreedath/slm-gemma-2b-qa
Model Size	2b
Required VRAM	5.2 GB
Updated	2026-07-18
Maintainer	thesreedath
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	4.46.3
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Slm Gemma 2B Qa 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 thesreedath/slm-gemma-2b-qa.