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Discover how to implement a simple Postgres logger for OpenAI endpoints, with demonstrations of local and remote database setups for tracking API calls, useful for evaluations and fine-tuning.
Master time-series forecasting using transformer models like Chronos and PatchTST, including their architectures, implementation differences, and practical applications through hands-on demonstrations.
Explore Qwen3 models through inference techniques, benchmarking with llmperf, and implementing MCP agents, with practical demonstrations using vLLM and SGLang frameworks.
Master the advanced fine-tuning techniques for leading language models like Gemma 3, Qwen3, Llama 4, Phi 4, and Mistral Small, with practical demonstrations using Unsloth, transformers, and vLLM for faster evaluations.
Explore the performance comparison between Google's TPUs and Nvidia GPUs for AI inference, with detailed benchmarking results, hardware specifications, and parallel processing approaches.
Master practical techniques for evaluating Large Language Models (LLMs), from building evaluation pipelines to analyzing performance metrics and comparing different models systematically.
Dive into the technical innovations of Deepseek v3, exploring performance comparisons, training efficiencies, architecture evolution, and optimization techniques for advanced AI model deployment.
Master multimodal audio and text processing using Qwen 2, from model architecture to practical implementation. Learn LoRA fine-tuning, data preparation, and deployment strategies for audio-text applications.
Master advanced LLM evaluation techniques through hands-on demonstrations using touch rugby examples, focusing on creating high-quality prompts, implementing few-shot learning, and optimizing pipeline performance.
Master efficient GPU utilization by implementing multiple LoRA adapters, from basic LoRaX setup to advanced vLLM deployment with Redis integration and proxy server configuration.
Explore different approaches to solving the ARC Prize challenge, including Domain Specific Languages, LLM-guided programming, and test-time training with neural networks and search algorithms.
Explore how to create secure Python sandboxes for AI agents to execute code, comparing different sandboxing methods including Docker, Pyodide, and MCP-run-python with practical demonstrations.
Explore advanced techniques for preparing and visualizing data when fine-tuning Large Language Models, including synthetic data generation, document ingestion, chunking approaches, and evaluation dataset creation.
Explore robotics AI model fundamentals, comparing SO-101 robot options, leader-follower setups, and key differences between ACT, GR00T, and pi0 models for hands-on learning.
Master GitHub repository analysis using Gitingest MCP server to fetch summaries, folder structures, and content for enhanced code understanding and development workflows.
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