from transformers import AutoModel, AutoTokenizer

from peft import PeftModel Load the base model and tokenizer

base_model_name = "intfloat/multilingual-e5-large-instruct"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

base_model = AutoModel.from_pretrained(base_model_name) Load the LoRA adapter directly

adapter_repo = "IslamQA/multilingual-e5-large-instruct-finetuned"

model = PeftModel.from_pretrained(base_model, adapter_repo)

An embedding model optimized for retrieving passages that answer questions about Islam. The passages are inherently multilingual, as they contain quotes from the Quran and Hadith. They often include preambles like "Bismillah" in various languages and follow a specific writing style.

from transformers import AutoModel, AutoTokenizer from peft import PeftModel

base_model_name = "intfloat/multilingual-e5-large-instruct" tokenizer = AutoTokenizer.from_pretrained(base_model_name) base_model = AutoModel.from_pretrained(base_model_name)

adapter_repo = "IslamQA/multilingual-e5-large-instruct-finetuned" model = PeftModel.from_pretrained(base_model, adapter_repo)