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What tools and frameworks has John Mosquera used for building and training machine learning models in his recent personal projects?
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John has been actively using a wide range of tools for building and training ML models. He's proficient with foundational libraries like TensorFlow, PyTorch, Scikit-Learn, and Keras. For natural language processing, he leverages Hugging Face Transformers and LangChain. He also utilizes optimization techniques with CUDA and explores distributed training with TPUs. To track and manage his experiments, he uses Weights & Biases (W&B) and MLflow. His focus on efficient fine-tuning is evident through his work with LoRA, QLoRA, Adapter Training, and Unsloth, as well as techniques like flash attention. John Mosquera has used TensorFlow, PyTorch, Scikit-Learn, Keras, LangChain, Hugging Face Transformers, CUDA, TPUs, Weights & Biases (W&B), MLflow, LoRA, QLoRA, Adapter Training, and Unsloth for building and training ML models in his personal projects.