What are the hardware requirements for Gemma 4 12B Sv Source2synth Nlu Lora?

Gemma 4 12B Sv Source2synth Nlu Lora requires approximately 0.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 12B Sv Source2synth Nlu Lora and how large is it?

Gemma 4 12B Sv Source2synth Nlu Lora is developed by Sinall, a model with 12b 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 12B Sv Source2synth Nlu Lora?

Gemma 4 12B Sv Source2synth Nlu Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma 4 12B Sv Source2synth Nlu Lora by Sinall — VRAM 0.3GB

Name: Gemma 4 12B Sv Source2synth Nlu Lora
Author: Sinall

Gemma 4 12B Sv Source2synth Nlu Lora is an open-source language model by Sinall. Features: 12b LLM, VRAM: 0.3GB, License: apache-2.0.

Arxiv:2409.08239 Adapter Base model:adapter:google/gemm... Base model:google/gemma-4-12b Conversational Finetuned Lora Peft Region:us Safetensors Sft Source2synth Sv Swedish Synthetic-data Trl

Model Card on HF 🤗: https://huggingface.co/Sinall/gemma-4-12B-sv-source2synth-nlu-lora

Gemma 4 12B Sv Source2synth Nlu Lora Parameters and Internals

LLM Name	Gemma 4 12B Sv Source2synth Nlu Lora
Repository 🤗	https://huggingface.co/Sinall/gemma-4-12B-sv-source2synth-nlu-lora
Base Model(s)	Gemma 4 12B google/gemma-4-12B
Model Size	12b
Required VRAM	0.3 GB
Updated	2026-10-17
Maintainer	Sinall
Model Files	0.3 GB
Supported Languages	sv
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|gate_proj\|k_proj\|down_proj\|o_proj\|q_proj\|up_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Gemma 4 12B Sv Source2synth Nlu Lora 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 Sinall/gemma-4-12B-sv-source2synth-nlu-lora.