Tags: large language model*

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  1. Summarizer Service is a bare-bones sample Flask web application using Flask, using subprocesses and custom scripts. The application provides text web page summarization for bookmarking services, accepting both GET and POST requests with or without a custom prompt.
  2. Explore the best LLM inference engines and servers available to deploy and serve LLMs in production, including vLLM, TensorRT-LLM, Triton Inference Server, RayLLM with RayServe, and HuggingFace Text Generation Inference.
    2024-06-21 Tags: , , by klotz
  3. A new model developed by researchers at MIT and the University of Washington predicts human goals or actions more accurately than previous models. The latent inference budget model identifies patterns in human or machine decision-making and uses this information to forecast behavior.
  4. This guide demonstrates how to execute end-to-end LLM workflows for developing and productionizing LLMs at scale. It covers data preprocessing, fine-tuning, evaluation, and serving.
  5. The Allen Institute for AI has released the Tulu 2.5 suite, a collection of advanced AI models trained using Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). The suite includes a variety of models trained on various datasets to enhance their reward and value models. This release aims to significantly improve language model performance across several domains.
  6. This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
    2024-06-21 Tags: , , , , , by klotz
  7. 2024-06-21 Tags: , , , , by klotz
  8. use of the LLM to perform next-token prediction, and then convert the predicted next token into a classification label.
    2024-06-21 Tags: , by klotz
  9. LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
  10. Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.
    2024-06-19 Tags: , , , by klotz

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