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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.
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.
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.
Google's Gemma 3 open-weight model shows significant improvements over Gemma 2 and even outperforms GPT-4 in some tests. This article examines its performance across various benchmarks.
An in-depth comparison of Google's Gemma 3 model using Quantization Aware Training (QAT) versus FP16 precision, examining speed, accuracy, and practical applications.