Service Development Kit that uses Terraform, AWS ECS, Rust, Actix App, Postgress RDS, LLM, RAG, Cloudflare
• step-by-step guide on how to set up the service development kit, including creating an SSL certificate, setting up Terraform, and configuring Cloudflare.
• Rust, LLM, and RAG in the service development kit.
• A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
The Pipe is a multimodal-first tool for feeding files and web pages into vision-language models such as GPT-4V. It is best for LLM and RAG applications that want to support comprehensive textual and visual understanding across a wide range of data sources. The Pipe is available as a 24/7 hosted API at thepi.pe, or it can be set up locally to let you run the compute.
Scrapegraph-ai is a Python library for web scraping using AI. It provides a SmartScraper class that allows users to extract information from websites using a prompt. The library uses LLM models like Ollama, OpenAI, Azure, Gemini, and others for information extraction.
Infrastructure observability companies such as New Relic, Datadog, Dynatrace, Elastic and Splunk are actively enhancing their platforms through the integration of LLMs.
Mistral.rs is a fast LLM inference platform supporting inference on a variety of devices, quantization, and easy-to-use application with an Open-AI API compatible HTTP server and Python bindings. It supports the latest Llama and Phi models, as well as X-LoRA and LoRA support. The project aims to provide the fastest LLM inference platform possible.
AutoCrawler is a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding and aims to assist crawlers in handling diverse and changing web environments more efficiently. This work introduces a crawler generation task for vertical information web pages and proposes the paradigm of combining LLMs with crawlers, which supports the adaptability of traditional methods and enhances the performance of generative agents in open-world scenarios. Generative agents, empowered by large language models, suffer from poor performance and reusability in open-world scenarios.
This post explores the use and implications of temperature scaling in the language model output generation, specifically discussing its relationship to the beam search heuristic.
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