What are the hardware requirements for Gemma2 2B Code Lora?

Gemma2 2B Code Lora requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma2 2B Code Lora and how large is it?

Gemma2 2B Code Lora is developed by bunnycore, 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 Gemma2 2B Code Lora?

Gemma2 2B Code Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Gemma2 2B Code Lora by bunnycore — VRAM 0.1GB

Name: Gemma2 2B Code Lora
Author: bunnycore

Gemma2 2B Code Lora is an open-source language model by bunnycore. Features: 2b LLM, VRAM: 0.1GB, License: apache-2.0, LLM Explorer Score: 0.14.

Base model:finetune:ilyagusev/... Base model:ilyagusev/gemma-2-2... En Endpoints compatible Gemma2 Lora Region:us Safetensors Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/bunnycore/Gemma2-2b-code-lora

Gemma2 2B Code Lora Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, gemma2, trl

Additional Notes

This Gemma2 model was trained 2x faster using Unsloth and Huggingface's TRL library.

LLM Name	Gemma2 2B Code Lora
Repository 🤗	https://huggingface.co/bunnycore/Gemma2-2b-code-lora
Base Model(s)	Gemma 2 2B It Abliterated IlyaGusev/gemma-2-2b-it-abliterated
Model Size	2b
Required VRAM	0.1 GB
Updated	2026-07-25
Maintainer	bunnycore
Model Files	0.1 GB
Supported Languages	en
Model Architecture	AutoModel
License	apache-2.0
Model Max Length	8192
Is Biased	none
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	o_proj\|gate_proj\|down_proj\|q_proj\|v_proj\|k_proj\|up_proj
LoRA Alpha	16
LoRA Dropout	0
R Param	16

Rank the Gemma2 2B Code 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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Telugu Gemma 2B Finetuned Sft	0K / 0.3 GB	0	3
...arge.clip Laion2b Soup Ft In1k	0K / 3.4 GB	12376	5
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Note: green Score (e.g. "73.2") means that the model is better than bunnycore/Gemma2-2b-code-lora.