Lina-Z/arabic_llama_model

LLaMA 3.3 70B Instruct – Arabic Fine-Tuned

Model Description

This model is a fine-tuned version of LLaMA 3.3 70B Instruct, adapted to better understand and generate high-quality Arabic text.

The fine-tuning process focused on enhancing the model’s performance in Arabic across multiple tasks, including:

• Instruction following
• Question answering
• Reasoning
• General conversational capabilities

The model retains the strong multilingual and reasoning capabilities of the original LLaMA 3.3 70B Instruct while improving Arabic fluency, comprehension, and alignment.

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Base Model

• Base model: meta-llama/Llama-3.3-70B-Instruct
• Architecture: Decoder-only Transformer
• Model size: 70 billion parameters
• Context length: Same as the base model

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Training Details

• Fine-tuning type: LoRA / PEFT
• Training data: Curated Arabic instruction-following datasets
• Languages: Arabic (primary), English (secondary)
• Objective: Improve Arabic instruction-following, reasoning, and generation quality

No additional pretraining was performed. The model was fine-tuned starting from the original LLaMA 3.3 70B Instruct checkpoint.

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Intended Use

This model is intended for:

• Arabic conversational agents
• Arabic question answering
• Instruction-following tasks
• Content generation in Arabic
• Research and experimentation with Arabic LLMs

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Limitations

• The model may still produce incorrect or biased outputs.
• Performance may vary across Arabic dialects.
• Not suitable for high-risk or safety-critical applications without further evaluation.

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Ethical Considerations

This model inherits limitations and potential biases from both the base model and the fine-tuning data. Users should apply appropriate safety measures and human oversight when deploying the model.

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Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Lina-Z/arabic_llama_model"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

prompt = "اشرح مفهوم الذكاء الاصطناعي باختصار."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))