What are the hardware requirements for Gemma 4 26bFT LuxAlpha Code MoE?

Gemma 4 26bFT LuxAlpha Code MoE requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 26bFT LuxAlpha Code MoE and how large is it?

Gemma 4 26bFT LuxAlpha Code MoE is developed by lshar, a model with 26b 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 4 26bFT LuxAlpha Code MoE?

Gemma 4 26bFT LuxAlpha Code MoE is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 4 26bFT LuxAlpha Code MoE by lshar — VRAM 0.1GB

Name: Gemma 4 26bFT LuxAlpha Code MoE
Author: lshar

Gemma 4 26bFT LuxAlpha Code MoE is an open-source language model by lshar. Features: 26b LLM, VRAM: 0.1GB, License: gemma.

Adapter Amd-rocm Base model:adapter:google/gemm... Base model:google/gemma-4-26b-... Bigcodebench Code Conversational En Finetuned Gemma4 Gsm8k Humanevalplus Lora Mi300x Mixture-of-experts Model-index Moe Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/lshar/gemma-4-26bFT-LuxAlpha_code-MoE

Gemma 4 26bFT LuxAlpha Code MoE Parameters and Internals

LLM Name	Gemma 4 26bFT LuxAlpha Code MoE
Repository 🤗	https://huggingface.co/lshar/gemma-4-26bFT-LuxAlpha_code-MoE
Base Model(s)	Gemma 4 26B A4B It google/gemma-4-26B-A4B-it
Model Size	26b
Required VRAM	0.1 GB
Updated	2026-09-14
Maintainer	lshar
Model Files	0.1 GB
Supported Languages	en code
Model Architecture	Adapter
License	gemma
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj.linear\|down_proj.linear\|up_proj.linear\|gate_proj.linear\|k_proj.linear\|o_proj.linear\|v_proj.linear
LoRA Alpha	64
LoRA Dropout	0
R Param	32

Rank the Gemma 4 26bFT LuxAlpha Code MoE 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

Best Alternatives to Gemma 4 26bFT LuxAlpha Code MoE

Best Alternatives	Context / RAM	Downloads	Likes
Dark Goetia 26B A4B LoRA V2	0K / 0 GB	198	2
Dark Goetia 26B A4B LoRA V4	0K / 0 GB	20	1

Note: green Score (e.g. "73.2") means that the model is better than lshar/gemma-4-26bFT-LuxAlpha_code-MoE.