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Fine Tuning Gemma 2B It For Arabic by Ruqiya

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Fine Tuning Gemma 2B It For Arabic is an open-source language model by Ruqiya. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, LLM Explorer Score: 0.13.

  Ar Base model:finetune:google/gem...   Base model:google/gemma-2b-it   Conversational   Dataset:arbml/cidar   En   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic 

Fine Tuning Gemma 2B It For Arabic Benchmarks

LLME Score: 0.12737
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Fine Tuning Gemma 2B It For Arabic (Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic)
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Fine Tuning Gemma 2B It For Arabic Parameters and Internals

Model Type 
text-generation
Supported Languages 
Arabic (high), English (moderate)
Training Details 
Data Sources:
arbml/CIDAR
Methodology:
Fine-tuning on Arabic dataset
Input Output 
Input Format:
Chat template input for text generation.
Accepted Modalities:
text
Output Format:
Generated text based on the input prompt.
LLM NameFine Tuning Gemma 2B It For Arabic
Repository 🤗https://huggingface.co/Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic 
Base Model(s)  Gemma 2B It   google/gemma-2b-it
Model Size2b
Required VRAM5.1 GB
Updated2026-03-29
MaintainerRuqiya
Model Typegemma
Model Files  5.0 GB: 1-of-2   0.1 GB: 2-of-2
Supported Languagesar en
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.38.0
Tokenizer ClassGemmaTokenizer
Padding Token<eos>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic.

Rank the Fine Tuning Gemma 2B It For Arabic Capabilities

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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  
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