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@@ -23,7 +23,7 @@ This model is designed for **cross-lingual question answering** using Retrieval-
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  It can take documents in multiple languages — Urdu, Hindi, Spanish, English — and answer in the same or different language.
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  ## Key Features
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- - **LLM Used:** LLaMA 2 / Mistral (depending on runtime availability in Colab)
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  - **Embedding Model:** `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`
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  - **RAG Pipeline:** FAISS-based vector search + context injection
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  - **Training/Processing:** Implemented entirely in Google Colab using open-source tools only
 
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  It can take documents in multiple languages — Urdu, Hindi, Spanish, English — and answer in the same or different language.
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  ## Key Features
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+ - **LLM Used:** Meta-Llama-3-8B-Instruct
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  - **Embedding Model:** `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`
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  - **RAG Pipeline:** FAISS-based vector search + context injection
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  - **Training/Processing:** Implemented entirely in Google Colab using open-source tools only