Open source generative AI models can be downloaded for free, used at scale without racking up API call costs, and run securely behind corporate firewalls. But don’t let your guard down. Risks still exist and some aren’t only magnified, but new ones specific to gen AI are emerging.
The race to bring data into development is here. We see it in many ways, most noticeably in the increasing relevance of time series databases. InfluxDB, from InfluxData, is one of the leading open source time series database technology providers. The company is now partnering with Amazon Web Services to provide a managed open source service for time series database services.
- noTunes is a macOS application that prevents iTunes or Apple Music from launching.
- It can be toggled on/off via the menu bar icon.
- The application has a MIT license and is open-source.
- It can be installed via Homebrew or direct download.
- The application has a simple and intuitive user interface.
- It can be toggled on/off via the menu bar icon.
- The application has a MIT license and is open-source.
- It can be installed via Homebrew or direct download.
- The application has a simple and intuitive user interface.
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.
