Phind introduces its latest model, Phind Model V7, which is faster and outperforms GPT-4 in coding tasks. It achieves a HumanEval score of 74.7%, fine-tuned with 70B+ tokens of high-quality code. Notably, it runs five times faster than GPT-4, thanks to NVIDIA's TensorRT-LLM library. The model supports up to 16k tokens, allowing 12k tokens for user input on the website
3D-GPT is a pioneering framework that simplifies 3D asset modeling in the metaverse era by utilizing large language models (LLMs). Developed collaboratively by teams from the Australian National University, University of Oxford, and Beijing Academy of Artificial Intelligence, 3D-GPT breaks down complex 3D modeling tasks into manageable segments, employing LLMs as adept problem-solvers. The framework consists of three key agents – task dispatch, conceptualization, and modeling – working together to enhance initial scene descriptions and seamlessly integrate procedural generation. Demonstrating reliability and effective collaboration with human designers, 3D-GPT not only streamlines traditional 3D modeling but also integrates smoothly with Blender, expanding manipulation possibilities. This innovative approach underscores the substantial potential of language models in shaping the future of 3D modeling, particularly in scene generation and animation.
The gpt-prompt-engineer tool is a powerful solution for prompt engineering, enabling users to experiment and find the optimal prompt for GPT-4 and GPT-3.5-Turbo language models. It generates a variety of prompts based on the provided use-case and test cases, and then tests and ranks them using an ELO rating system. Additionally, there is a specific classification version that evaluates test case correctness and provides scores for each prompt. The tool also supports optional logging to Weights & Biases, allowing for tracking of configurations and prompt performance.
OlaGPT is a newly developed framework that enhances large language models by simulating human-like problem-solving abilities. It incorporates six cognitive modules, including attention, memory, reasoning, learning, decision-making, and action selection. The model was evaluated on algebraic word problems and analogical reasoning questions, showing superior performance against existing benchmarks. OlaGPT is integrated with pre-existing models such as GPT-3 as base models, with different cognitive modules added. Although the framework has limitations that prevent it from providing a creative solution, it is a promising tool that could approximate the human brain model.
https://www.mlwires.com/2023/06/olagpt-boosts-llms-with-human-like.html
DreamGPT, the first GPT-based solution that uses hallucinations from LLMs for divergent thinking to generate new innovative ideas. Hallucinations are often seen as a negative thing, but what if they could be used for our advantage? dreamGPT is here to show you how. The goal of dreamGPT is to explore as many possibilities as possible, as opposed to most other GPT-based solutions which are focused on solving specific problems.
Tree-of-Thought (ToT) aims to enhance the problem-solving capabilities of large language models (LLMs) like GPT-4. The framework utilizes a deliberate 'System 2' tree search approach to tackle complex and general problems that LLMs struggle with. The author demonstrates significant improvements on three tasks: the game of 24, creative writing, and crosswords, which GPT-4 and CoT (chain of thought, another approach) find challenging due to the need for planning and searching. The limitations of token-by-token decoding, which lacks lookahead, backtrack, and global exploration, are highlighted as the reason for these difficulties. ToT achieves a tenfold performance boost by leveraging the LLM's ability to generate diverse intermediate thoughts, self-evaluate them through deliberate reasoning, and employ search algorithms like breadth-first search (bfs) or depth-first search (dfs) to systematically explore the problem space.
SmartGPT is an experimental program meant to provide LLMs (particularly GPT-3.5 and GPT-4) with the ability to complete complex tasks without user input by breaking them down into smaller problems, and collecting information using the internet and other external sources.
An experiment where an autonomous GPT (Generative Pre-trained Transformer) agent is given access to a browser to perform tasks. For example adding text to a webpage and making restaurant reservations. gpt-assistant requires Node.js, an OpenAI API key, and a Postgres database.
LocalAI is an API that can be used as a replacement for OpenAI, which supports various models and can run on consumer-grade hardware. It supports ggml compatible models such as LLaMA, alpaca, gpt4all, vicuna, koala, gpt4all-j, cerebras. It uses C bindings for faster inference and performance and comes as a container image that can be run with docker-compose. The API can be used for running text generation as a service, following the OpenAI reference.
MiniChain is a library used to link prompts together in a sequence, with the ability to manipulate and visualize them using Gradio. Users can ensure the prompt output matches specific criteria through the use of data classes and typed prompts. The library does not manage documents or provide tools, but suggests using the Hugging Face Datasets library for that purpose. Additionally, users can include their own backends.
The AutoGPT website offers an AI buddy that can be set up with initial roles and goals without the requirement of human supervision. This AI tool automatically leverages all available resources to achieve your set goal. The tool is inspired by Auto-GPT and features internet access for information gathering and searches. It also allows users to save chat history, credentials, and definition of AI directly in the browser.
The paper explores the potential of developing autonomous cooperation for conversational language models without relying heavily on human input. The proposed framework named role-playing utilizes inception prompting to direct chat agents toward tasks that align with human intentions while enhancing consistency. The role-playing framework produces conversational data for investigating the behaviors and capabilities of language models, resulting in a valuable resource for studying conversational language models. The authors' contributions include the introduction of a novel communicative agent framework, offering a scalable approach for investigating multi-agent systems, and making the library available for further research on communicative agents.
This project aimed to develop a minimal autonomous llm agent using Python code and shell commands, with minimal external dependencies. The agent is expected to solve the objective without relying on additional resources beyond what is provided.
Long Stable Diffusion is a pipeline of generative models that can be used to illustrate a full story. Currently, Stable Diffusion can only take in a short prompt, but Long Stable Diffusion can generate images for a long-form text. The process involves starting with a long-form text, asking GPT-3 for several illustration ideas for the beginning, middle, and end of the story, translating the ideas to "prompt-English," and then putting them through Stable Diffusion to generate the images. The images and prompts are then dumped into a .docx file for easy copy-pasting. The purpose of this pipeline is to automate the process of generating illustrations for AI-generated stories.
Taxy AI is an organization focused on transforming the role of AI in human-computer interaction. They currently offer a self-hosted browser extension that interacts with a user's browser to execute natural language commands. The organization is working towards implementing new features such as voice-to-command, support for future LLMs, and auto-streamlined workflows.
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
A minimalist node.js local client to use GPT-3.5 or GPT-4.
Demonstration tutorial of retraining OpenAI’s GPT-2-small (a text-generating Transformer neural network) on a large public domain Project Gutenberg poetry corpus to generate high-quality English verse.
https://jalammar.github.io/illustrated-gpt2/
Other tutorial : https://medium.com/@ngwaifoong92/beginners-guide-to-retrain-gpt-2-117m-to-generate-custom-text-content-8bb5363d8b7f
https://github.com/minimaxir/gpt-2-simple
Example : http://textsynth.org/
Datasets :
https://www.kaggle.com/datasets
https://github.com/awesomedata/awesome-public-datasets
Scrap webpage with python :
https://www.crummy.com/software/BeautifulSoup/
https://github.com/EugenHotaj/beatles/blob/master/scraper.py
https://github.com/shawwn/colab-tricks
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