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--- |
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base_model: unsloth/Mistral-Nemo-Base-2407-bnb-4bit |
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library_name: transformers |
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language: |
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- ar |
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pipeline_tag: text-generation |
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datasets: |
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- MahmoudIbrahim/Arabic_NVIDIA |
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--- |
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- **Developed by:** Mahmoud Ibrahim |
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- |
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**How to use :** |
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``` bush |
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! pip install transformers bitsandbytes |
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``` |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from IPython.display import Markdown |
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import textwrap |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("MahmoudIbrahim/Mistral_12b_Arabic") |
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model = AutoModelForCausalLM.from_pretrained("MahmoudIbrahim/Mistral_12b_Arabic",load_in_4bit =True) |
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alpaca_prompt = """فيما يلي تعليمات تصف مهمة، إلى جانب مدخل يوفر سياقاً إضافياً. اكتب استجابة تُكمل الطلب بشكل مناسب. |
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### التعليمات: |
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{} |
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### الاستجابة: |
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{}""" |
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# Format the prompt with instruction and an empty output placeholder |
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formatted_prompt = alpaca_prompt.format( |
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"كيف يمكن للحكومة المصرية والمجتمع ككل أن يعززوا من قدرة البلاد على تحقيق التنمية المستدامة؟ " , # instruction |
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"" # Leave output blank for generation |
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) |
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# Tokenize the formatted string directly |
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input_ids = tokenizer.encode(formatted_prompt, return_tensors="pt") # Use 'cuda' if you want to run on GPU |
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def to_markdown(text): |
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text = text.replace('•','*') |
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return Markdown(textwrap.indent(text, '>', predicate=lambda _: True)) |
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# Generate text |
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output = model.generate( |
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input_ids, |
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max_length=128, # Adjust max length as needed |
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num_return_sequences=1, # Number of generated responses |
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no_repeat_ngram_size=2, # Prevent repetition |
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top_k=50, # Filter to top-k tokens |
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top_p=0.9, # Use nucleus sampling |
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temperature=0.7 , # Control creativity level |
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) |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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to_markdown(generated_text) |
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``` |
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**The model response :** |
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