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.
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.
Jemma is a GitHub repository that utilizes AI agents to build software from text-based ideas
It takes an idea in text form and assembles a team to create a web-based prototype
- Works best with Claude models, such as claude-3-haiku-20240307
- Project managers, business owners, and engineers work together to create prototypes
- Users can provide feedback to improve the prototypes
This tutorial introduces promptrefiner, a tool created by Amirarsalan Rajabi that uses the GPT-4 model to create perfect system prompts for local LLMs.
This GitHub repository contains a course on Large Language Models (LLMs) with roadmaps and Colab notebooks. The course is divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. Each part covers various topics, including mathematics, Python, neural networks, instruction datasets, pre-training, supervised fine-tuning, reinforcement learning from human feedback, evaluation, quantization, new trends, running LLMs, building a vector storage, retrieval augmented generation, advanced RAG, inference optimization, and deployment.
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.
This GitHub repository contains information on fine-tuning large language models (LLMs).
It includes a Jupyter Notebook file named "7 Steps to Fine-Tune LLMs.ipynb" that outlines the process.
Users can find information on setting up the environment, preparing data, fine-tuning models, evaluating performance, and saving fine-tuned models.
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
- The Open Interpreter repository provides a natural language interface for computers.
- It enables users to interact with their computer systems through a chat-like interface in the terminal.
- Open Interpreter supports various programming languages, including Python, Javascript, Shell, and more.
- The repository offers installation instructions, usage examples, and an interactive demo.
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