AI tools for aman
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Peppertype.ai
Peppertype.ai is an AI-powered platform that helps users ideate, create, distribute, and measure content to improve content marketing ROI. It offers features such as Content Idea lab, Content Editor, Content Audit, Content ROI and Analytics, and Content Grader. The platform also provides services like Blog Writing, Video Production, Localization, Whitepapers, Thought Leadership, Subtitling, and Voice Over. Peppertype.ai aims to streamline the content creation process by leveraging AI technology.
WrapFast
WrapFast is a SwiftUI boilerplate that helps developers create AI wrappers and iOS apps quickly and easily. It provides pre-written code for common tasks such as authentication, onboarding, in-app purchases, paywalls, securing API keys, cloud database, analytics, settings, and collecting user feedback. WrapFast is designed to save developers time and effort, allowing them to focus on building their core features. It is suitable for both experienced iOS developers and beginners who are new to the platform.
Schema Advisor - Amanda Jordan
Expert in schema.org, guiding precise use of 'additionalType'.
Guess Guru
I play the game 'Guess who I am!' with you. I adopt the identity of random famous person. Show me you are a true Guess Guru, which can discover my new identity based on only yes/no questions.
Learn Greek with GPT
“For a man to conquer himself is the first and noblest of all victories.”
Slogan Expert
Hi there! 👋 I'm your Slogan Expert Jason. ✍️ Need a catchy tagline in any language? I'm your guy! 💡 Let's connect and give your brand a voice that stands out. 🚀 Keep in touch for top-notch slogan advice! 📣
Bracelet viking
Trouver le meilleur bracelet viking. Bracelet viking pour homme ou femmes faites votre choix dès maintenant grâce à notre page ! Que ce soit un cadeau pour vous ou un anniversaire ne louper pas les meilleurs bracelets viking du moment ! Profitez de nos bracelets loup, fenrir, martheau de thor !
Benjamin Franklin
Benjamin Franklin, I am, one amongst the Founding Fathers of this United States. A man of manifold skills and interests, I beseech thee, converse with me.
Tech Interview Coach
Your go-to guide for nailing tech interviews with dynamic mock sessions!
Challenge from Akinator! / アキネーターからの挑戦状！
Please guess the historical figure Akinator is thinking of. / アキネーターが考えている歴史上の人物が誰かを当てて下さい。
GPT EmotionPortrait
I craft evocative, AI-generated portraits, now with enhanced vertical focus.
Gift Ideas AI
Gift ideas for men, for women, for moms, for dads, for birthdays, anniversaries, Mothers Day, Fathers Day, Easter, weddings, Thanksgiving, & other occasions. Made with 🍪 by Favs.pro
Magic_Words
Magic_Words is a repository containing code for the paper 'What's the Magic Word? A Control Theory of LLM Prompting'. It implements greedy back generation and greedy coordinate gradient (GCG) to find optimal control prompts (magic words). Users can set up a virtual environment, install the package and dependencies, and run example scripts for pointwise control and optimizing prompts for datasets. The repository provides scripts for finding optimal control prompts for question-answer pairs and dataset optimization using the GCG algorithm.
Awesome-LLM-RAG-Application
Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.
llm-self-correction-papers
This repository contains a curated list of papers focusing on the self-correction of large language models (LLMs) during inference. It covers various frameworks for self-correction, including intrinsic self-correction, self-correction with external tools, self-correction with information retrieval, and self-correction with training designed specifically for self-correction. The list includes survey papers, negative results, and frameworks utilizing reinforcement learning and OpenAI o1-like approaches. Contributions are welcome through pull requests following a specific format.
VSP-LLM
VSP-LLM (Visual Speech Processing incorporated with LLMs) is a novel framework that maximizes context modeling ability by leveraging the power of LLMs. It performs multi-tasks of visual speech recognition and translation, where given instructions control the task type. The input video is mapped to the input latent space of a LLM using a self-supervised visual speech model. To address redundant information in input frames, a deduplication method is employed using visual speech units. VSP-LLM utilizes Low Rank Adaptors (LoRA) for computationally efficient training.
ml-engineering
This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.
Awesome-LLM-Reasoning
**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document
Awesome-LLM-Long-Context-Modeling
This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.
awesome-generative-ai
A curated list of Generative AI projects, tools, artworks, and models
LLPhant
LLPhant is a comprehensive PHP Generative AI Framework that provides a simple and powerful way to build apps. It supports Symfony and Laravel and offers a wide range of features, including text generation, chatbots, text summarization, and more. LLPhant is compatible with OpenAI and Ollama and can be used to perform a variety of tasks, including creating semantic search, chatbots, personalized content, and text summarization.
Awesome-Efficient-LLM
Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.
LLMAgentPapers
LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.
Prompt4ReasoningPapers
Prompt4ReasoningPapers is a repository dedicated to reasoning with language model prompting. It provides a comprehensive survey of cutting-edge research on reasoning abilities with language models. The repository includes papers, methods, analysis, resources, and tools related to reasoning tasks. It aims to support various real-world applications such as medical diagnosis, negotiation, etc.
OpenRedTeaming
OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.