What are the hardware requirements for Tunelm Gemma4 4B Sft Quality 20260927 86 Best?

Tunelm Gemma4 4B Sft Quality 20260927 86 Best requires approximately 15.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tunelm Gemma4 4B Sft Quality 20260927 86 Best and how large is it?

Tunelm Gemma4 4B Sft Quality 20260927 86 Best is developed by tensorfiend, a model with 4b 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 Tunelm Gemma4 4B Sft Quality 20260927 86 Best?

Tunelm Gemma4 4B Sft Quality 20260927 86 Best is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tunelm Gemma4 4B Sft Quality 20260927 86 Best by tensorfiend — VRAM 15.9GB

Name: Tunelm Gemma4 4B Sft Quality 20260927 86 Best
Author: tensorfiend

Tunelm Gemma4 4B Sft Quality 20260927 86 Best is an open-source language model by tensorfiend. Features: 4b LLM, VRAM: 15.9GB, License: apache-2.0, LLM Explorer Score: 0.31.

Base model:finetune:google/gem... Base model:google/gemma-4-e4b-... Conversational Endpoints compatible Gemma4 Image-text-to-text Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/tensorfiend/tunelm-gemma4-4b-sft-quality-20260927-86-best

Tunelm Gemma4 4B Sft Quality 20260927 86 Best Parameters and Internals

LLM Name	Tunelm Gemma4 4B Sft Quality 20260927 86 Best
Repository 🤗	https://huggingface.co/tensorfiend/tunelm-gemma4-4b-sft-quality-20260927-86-best
Base Model(s)	Gemma 4 E4B It google/gemma-4-E4B-it
Model Size	4b
Required VRAM	15.9 GB
Updated	2026-10-01
Maintainer	tensorfiend
Model Type	gemma4
Model Files	15.9 GB
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.17.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Tunelm Gemma4 4B Sft Quality 20260927 86 Best 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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