metadata

base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
library_name: transformers
datasets:
  - Yasbok/Alpaca_arabic_instruct
  - MahmoudIbrahim/Arabic_NVIDIA
language:
  - ar
pipeline_tag: text-generation
tags:
  - finance

Meta-LLama3-Instruct-Arabic

Meta-LLama3-Instruct-Arabic is a fine-tuned version of Meta's LLaMa model, specialized for Arabic language tasks. This model has been designed for a variety of NLP tasks including text generation,and language comprehension in Arabic.

Model Details

Model Name: Meta-LLama3-Instruct-Arabic
Base Model: LLaMa
Languages: Arabic
Tasks: Text Generation,Language Understanding
Quantization: [Specify if it’s quantized, e.g., 4-bit quantization with bitsandbytes, or float32]

Installation

To use this model, you need the unsloth andtransformers library from Hugging Face. You can install it as follows:

! pip install transformers bitsandbytes

how to use:

from transformers import AutoTokenizer, AutoModelForCausalLM
from IPython.display import Markdown
import textwrap 

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("MahmoudIbrahim/Meta-LLama3-Instruct-Arabic")
model = AutoModelForCausalLM.from_pretrained("MahmoudIbrahim/Meta-LLama3-Instruct-Arabic",load_in_4bit =True)


alpaca_prompt = """فيما يلي تعليمات تصف مهمة، إلى جانب مدخل يوفر سياقاً إضافياً. اكتب استجابة تُكمل الطلب بشكل مناسب.

### التعليمات:
{}

### الاستجابة:
{}"""

# Format the prompt with instruction and an empty output placeholder
formatted_prompt = alpaca_prompt.format(
    "ماذا تعرف عن الحضاره المصريه"   ,  # instruction
    ""  # Leave output blank for generation
)

# Tokenize the formatted string directly
input_ids = tokenizer.encode(formatted_prompt, return_tensors="pt")  # Use 'cuda' if you want to run on GPU

def to_markdown(text):
    text = text.replace('•','*')
    return Markdown(textwrap.indent(text, '>', predicate=lambda _: True))

# Generate text
output = model.generate(
    input_ids,
    max_length=128,            # Adjust max length as needed
    num_return_sequences=1,     # Number of generated responses
    no_repeat_ngram_size=2,     # Prevent repetition
    top_k=50,                   # Filter to top-k tokens
    top_p=0.9,                  # Use nucleus sampling
    temperature=0.7 ,            # Control creativity level
  
)

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
to_markdown(generated_text)