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An in-depth exploration of reinforcement learning techniques for language models, including supervised fine-tuning, ORPO, and GRPO, with practical implementations and performance analysis.
An in-depth look at Adam and AdamW optimization algorithms for training large language models. Explores the key differences and advantages of AdamW for improved generalization.
Discover how the Adam optimization algorithm combines momentum and RMSprop to accelerate neural network training. Learn about its implementation, hyperparameters, and widespread adoption in deep learning.
Learn how to fine-tune language models using Apple MLX, exploring model sizes, data diversity, and optimization techniques. This guide covers the process from basic fine-tuning to creating chat models.
Learn how to fine-tune language models for memorization of custom datasets. This guide covers data preparation, hyperparameter selection, and practical implementation steps.
Learn how to fine-tune the latest open-source language models like Gemma, Qwen, Llama, and Mistral using Unsloth and Transformers libraries. This guide covers data preparation, hyperparameter tuning, and evaluation techniques.
Learn how to fine-tune the latest open-source language models like Gemma, Qwen, Llama, and Mistral using Unsloth and Transformers libraries. This guide covers data preparation, hyperparameter tuning, and evaluation techniques.
An in-depth exploration of data science, covering key concepts in data analytics, artificial intelligence, and machine learning. Learn about career opportunities and skills needed to succeed in this rapidly growing field.
Explore the capabilities of small language models that can run on everyday devices. Learn how these compact AI models perform various tasks with minimal resources.