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ksm26 / Pretraining-LLMs

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Master the essential steps of pretraining large language models (LLMs). Learn to create high-quality datasets, configure model architectures, execute training runs, and assess model performance for efficient and effective LLM pretraining.

machine-learning text-generation data-preparation performance-assessment developer-advocacy ai-training hugging-face large-language-models llm-evaluation model-configuration meta-llama pretraining-llms training-runs high-quality-datasets model-initialization depth-upscaling cost-effective-pretraining
  • Updated Aug 7, 2024
  • Jupyter Notebook

ksm26 / Prompt-Compression-and-Query-Optimization

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Enhance the performance and cost-efficiency of large-scale Retrieval Augmented Generation (RAG) applications. Learn to integrate vector search with traditional database operations and apply techniques like prefiltering, postfiltering, projection, and prompt compression.

mongodb projection query-optimization search-relevance data-retrieval performance-optimization reranking data-security vector-search developer-advocacy query-processing vector-search-engine database-operations prefiltering cost-efficiency data-retrieval-and-display prompt-compression rag-applications postfiltering large-scale-applications
  • Updated Jul 23, 2024
  • Jupyter Notebook

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