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This project aims to develop a fine-tuned large language model (7B parameter-sharded LLaMA) capable of recommending products based on text prompts

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praxton74/LLM-Product-Recommendation

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πŸ¦™ LLaMA Product Recommender πŸ›οΈ The power of LLMs and incorporating product and department information, this model provides personalized and relevant product suggestions to users.

🌟 Key Features:

πŸ”„ Data Preprocessing: Combines product and department data into a comprehensive dataset.

🎯 Model Training: Fine-tunes a pre-trained LLM (e.g., LLaMA-2) using PEFT (parameter-efficient fine-tuning) for optimal performance.

πŸ’¬ Text Generation: Generates product recommendations based on user-provided text prompts.

πŸ“Š Evaluation: Assesses the model's performance using appropriate metrics to ensure accuracy and relevance.

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This project aims to develop a fine-tuned large language model (7B parameter-sharded LLaMA) capable of recommending products based on text prompts

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