The article discusses Perplexica, an open-source AI-powered search tool that aims to address the limitations of traditional and proprietary AI-powered search engines. The tool uses large language models (LLMs) like Mixtral and Gemini to understand and process user queries, delivering relevant and insightful results. It allows searches to be conducted locally, ensuring privacy, and employs information retrieval techniques to fetch relevant web pages based on user queries. Perplexica offers focus modes for specific types of questions, including All Mode, Writing Assistant Mode, Academic Search Mode, YouTube Search Mode, and Wolfram Alpha Search Mode.
Learn how to install the ChatTTS open-source text-to-speech model for conversational scenarios, supporting English and Chinese. The installation process is explained step-by-step, along with information on how to fix the tone and precautions for network requirements.
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.
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.
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.
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
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.
Bookmarks on this page are managed by an admin user. 