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  • Synthetic Data Generation for Fine-Tuning Custom Retrieval Models using Distilabel

    Synthetic Data Generation for Fine-Tuning Custom Retrieval Models using Distilabel

    Aniruddha Shrikhande

    Distilabel revolutionizes dataset creation by leveraging large language models and flexible pipelines. Generate custom, task-specific data with ease for advanced machine learning applications.

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  • A Practitioners Guide to Running Ollama models in Colab – Collama

    A Practitioners Guide to Running Ollama models in Colab – Collama

    Aniruddha Shrikhande

    Unlock the power of AI with Ollama using Google Colab. Run advanced language models effortlessly.

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  • Why Choosing the Right Learning Partner is Crucial for Enterprise Success

    Why Choosing the Right Learning Partner is Crucial for Enterprise Success

    Vaibhav Kumar

    Choosing the right learning partner ensures workforce readiness, aligns with trends, and drives enterprise competitiveness.

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  • Short-Term vs Long-Term Memory in AI Agents

    Short-Term vs Long-Term Memory in AI Agents

    Vaibhav Kumar

    Short-term and long-term memory in AI agents enhance decision-making, learning, and adaptability in diverse applications.

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  • AI-Powered Image-to-JSON Conversion for LLM Fine-Tuning Using ‘Outlines’

    AI-Powered Image-to-JSON Conversion for LLM Fine-Tuning Using ‘Outlines’

    Aniruddha Shrikhande

    Generate structured JSON datasets from images using Outlines, a Python library crafted for robust and accurate Large Language Model (LLM) interactions.

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  • How Caching Helps Improve the LLM Inference?

    How Caching Helps Improve the LLM Inference?

    Vaibhav Kumar

    Large Language Models (LLMs) have transformed various fields, from virtual assistants to real-time translation and personalized content creation. These models rely on a computationally intensive process called inference, where they predict the next token or word based on previous input. While powerful, the high resource demands of LLMs can hinder their practical deployment, especially in…

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  • Fine-Tuning LLMs for Domain-Specific Tasks using Unsloth

    Fine-Tuning LLMs for Domain-Specific Tasks using Unsloth

    Aniruddha Shrikhande

    Discover how to fine-tune language models using Unsloth with this hands-on guide, designed to help you create efficient, domain-specific AI solutions.

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  • A Hands-On Guide to Streamlining LLM Testing Process with DeepEval

    A Hands-On Guide to Streamlining LLM Testing Process with DeepEval

    Aniruddha Shrikhande

    Streamline your LLM testing process with DeepEval framework. Discover built-in metrics, Pytest-like testing, and create practical test cases for robust model evaluation.

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  • Colpali: Hands-On Guide to PDF Analysis with Qwen2VL

    Colpali: Hands-On Guide to PDF Analysis with Qwen2VL

    Aniruddha Shrikhande

    This guide explores PDF analysis using Colpali and Qwen2VL, highlighting step-by-step methods to extract insights with vision-language models.

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  • UpTrain: A Hands-On Guide to LLM Evaluation

    UpTrain: A Hands-On Guide to LLM Evaluation

    Aniruddha Shrikhande

    UpTrain is an open-source platform that simplifies LLM evaluation with customizable metrics, performance tracking, and seamless integration for reliable AI solutions.

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