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Comparison between RAG (Retrieval-Augmented Generative) and fine-tuning for Large Language Models (LLMs).

Rag Vs Finetuning: Which Is Best For Your LLM Applications?

RAG has external knowledge read whereas fine-tuning comes with training data. Refer to this RAG v/s Fine-tuning guide to know the use cases and benefits of both.

A guide on selecting the optimal pre-trained model for fine-tuning

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How To Choose The Best Pre-Trained Model For Fine-Tuning?

You just cannot guess and pick any model from the basket and train it. If the model is not compatible with your target task and framework, you won’t get the results.

Image on the relevance of vector databases and their association with Large Language Models (LLMs)

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Exploring The Need For Vector Databases & Its Relation With LLMs

Vectors databases make data management easy by storing vector embeddings using advanced storage, indexing, and query processing techniques. Learn their importance for LLMs through this article.

A comparison of the economic aspects between deploying open-source Large Language Models (LLMs) and OpenAI GPT models

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LLM Economics: Which is Cheaper to deploy Open Source LLMs Vs OpenAI GPT models?

If your request volume escalates to millions per day then ChatGPT will be very costly over time. Fine tuning open source LLMs deployed to AWS is more affordable.