This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
Chip Huyen analyzed 845 open source AI tool repositories on GitHub using keywords like gpt, llm, and generative ai. He categorized these into infrastructure, model development, application development, and applications layers. There was significant growth in application development and model development layers in 2023, with popular applications being coding, bots, and info aggregation. Tools like Qdrant, Pinecone, and LanceDB emerged in infrastructure layer. Notable contributors include lucidrains, ggerganov, Illyasviel, xtekky, and RNN-based models like RWKV.
Introduction to Pkl, a programming language designed for generating configuration files.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
Addresses limitations of static languages and general-purpose languages for configuration purposes.
Provides safety by catching validation errors before deployment.
Scales from simple to complex use-cases.
Enhanced with capabilities borrowed from general-purpose languages.
Familiar syntax and easy learning curve.
Built-in validation using type annotations.
Ability to publish packages and import them as dependencies in a project.
Language bindings for Swift, Go, Java, and Kotlin.
Editor support for IntelliJ, VS Code, and Neovim.
llm-tool provides a command-line utility for running large language models locally. It includes scripts for pulling models from the internet, starting them, and managing them using various commands such as 'run', 'ps', 'kill', 'rm', and 'pull'. Additionally, it offers a Python script named 'querylocal.py' for querying these models. The repository also come
- create a custom base image for a Cloud Workstation environment using a Dockerfile
. Uses:
Quantized models from
. Uses:
Quantized models from
The "LLM" toolkit provides a command-line utility and Python library for interacting with large language models. It enables users to run prompts from the terminal, store responses in SQLite databases, generate embeddings, and more. This comprehensive guide includes topics such as setup, usage, OpenAI models, other models, embeddings, plugins, model aliases, Python API, prompt templates, logging, related tools, CLI reference, contributing, and changelog.
The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
