What are the hardware requirements for Gemma Help Tiny Sft?

Gemma Help Tiny Sft requires approximately 5.1 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 Help Tiny Sft and how large is it?

Gemma Help Tiny Sft is developed by burtenshaw, 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 Help Tiny Sft?

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

Gemma Help Tiny Sft by burtenshaw — VRAM 5.1GB, 8K context

Name: Gemma Help Tiny Sft
Author: burtenshaw

Gemma Help Tiny Sft is an open-source language model by burtenshaw. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, License: apache-2.0, Quantized, LLM Explorer Score: 0.14.

4bit Base model:quantized:unsloth/g... Base model:unsloth/gemma-2b-bn... En Endpoints compatible Gemma Gguf Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/burtenshaw/gemma-help-tiny-sft

Gemma Help Tiny Sft Parameters and Internals

LLM Name	Gemma Help Tiny Sft
Repository 🤗	https://huggingface.co/burtenshaw/gemma-help-tiny-sft
Base Model(s)	Gemma 2B Bnb 4bit unsloth/gemma-2b-bnb-4bit
Model Size	2b
Required VRAM	5.1 GB
Updated	2026-07-28
Maintainer	burtenshaw
Model Type	gemma
Model Files	5.0 GB: 1-of-2 0.1 GB: 2-of-2 2.7 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	GemmaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.44.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	bfloat16

Rank the Gemma Help Tiny Sft 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 burtenshaw/gemma-help-tiny-sft.