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An in-depth look at the process of creating large language models, covering pre-training, post-training, data collection, evaluation, and system optimizations.
Explore the process of fine-tuning OpenAI's Whisper model for improved speech recognition in low-resource languages. Learn about parameter-efficient techniques like LoRA for optimizing model performance.
Learn how to fine-tune Gemma 3 models using Unsloth for custom datasets. This guide covers the entire process from setup to deployment.
Comprehensive benchmark comparing the new Mac Mini M4 processor against M3, AMD, and NVIDIA GPUs for running large language models. Analysis of speed, cost-efficiency, and power consumption.
Explore the inner workings of Transformer models, the architecture behind modern language models like GPT-3. Learn about their structure, components, and how they process and generate text.
Explore the role of positional encoding in Transformers, focusing on ROPE and methods for extending context length. Learn how these techniques impact model performance and generalization.
Learn how to create question-answer datasets for fine-tuning language models using open-source alternatives like Llama 2. This guide covers data cleaning, preparation, and automation techniques.
Discover how Microsoft's latest F3 models bring high-quality AI capabilities to your phone, combining compact size with impressive performance.