#typescript #artificial_intelligence #chatbot #chatgpt #javascript #langchain #large_language_models #llamaindex #low_code #no_code #openai #rag #react #typescript #workflow_automation

Flowise is a tool that makes it easy to build applications using Large Language Models (LLMs) with a drag-and-drop interface. You can quickly start by installing NodeJS and then installing Flowise using simple commands. It also supports deployment through Docker and various cloud services like AWS, Azure, and more. The benefit to you is that you can create customized LLM flows without needing to write complex code, making it easier and faster to develop your applications. Additionally, Flowise offers extensive documentation and community support to help you along the way.

https://github.com/FlowiseAI/Flowise

#jupyter_notebook #course #large_language_models #llm #machine_learning #roadmap

This course is designed to help you master Large Language Models (LLMs) in three main parts This section covers the basics of mathematics, Python, and neural networks necessary for understanding LLMs.
2. **The LLM Scientist** This part focuses on building applications with LLMs, such as running models locally or via APIs, creating vector storage for retrieval augmented generation (RAG), optimizing inference, deploying models, and securing them against vulnerabilities.

The benefit to you is that you will gain a comprehensive understanding of LLMs, from the foundational knowledge to advanced techniques for building and deploying powerful language models. This will enable you to create efficient, accurate, and secure LLM-based applications.

https://github.com/mlabonne/llm-course

#python #binary #decompile #large_language_models #reverse_engineering

LLM4Decompile is a powerful tool that helps convert binary code back into readable C source code. It uses large language models to decompile Linux x86_64 binaries, supporting different optimization levels. The tool has various models with high re-executability rates, meaning the decompiled code can often run correctly and pass tests. You can easily use it by following the quick start guide, which includes steps to set up the environment, preprocess the binary, and decompile it into C code. This tool is beneficial because it saves time and effort in understanding complex binary code, making it easier to analyze and modify software.

https://github.com/albertan017/LLM4Decompile

#python #agent #ai_societies #artificial_intelligence #communicative_ai #cooperative_ai #deep_learning #large_language_models #multi_agent_systems #natural_language_processing

CAMEL-AI is a community-driven project focused on multi-agent systems. It helps researchers study how AI agents interact and behave in large-scale environments. This platform supports tasks like data generation, task automation, and world simulation. By using CAMEL-AI, users can create complex scenarios where multiple agents collaborate to solve problems or generate synthetic data. The benefits include gaining insights into agent behaviors, improving decision-making processes, and enhancing collaboration among AI entities. It's open-source and easy to install via PyPI.

https://github.com/camel-ai/camel

#python #genai #gpt #gpt_4 #graphrag #knowledge_graph #large_language_models #llm #rag #retrieval_augmented_generation

LightRAG is a system that helps computers understand and answer questions better by using a special way of organizing information called a "graph." This graph shows how different pieces of information are connected, making it easier for the system to find related answers. It works fast and can handle complex questions by combining two types of searches: one that looks at specific details and another that looks at broader topics. This makes it very useful for answering questions that need both specific and general information. Users benefit from getting accurate and relevant answers quickly, which is helpful in many applications like customer service and document retrieval.

https://github.com/HKUDS/LightRAG

#python #asr #deeplearning #generative_ai #large_language_models #machine_translation #multimodal #neural_networks #speaker_diariazation #speaker_recognition #speech_synthesis #speech_translation #tts

NVIDIA NeMo is a powerful, easy-to-use platform for building, customizing, and deploying generative AI models like large language models (LLMs), vision language models, and speech AI. It lets you quickly train and fine-tune models using pre-built code and checkpoints, supports the latest model architectures, and works on cloud, data center, or edge environments. NeMo 2.0 is even more flexible and scalable, with Python-based configuration and modular design, making it simple to experiment and scale up. The main benefit is that you can create advanced AI applications faster, with less effort, and at lower cost, while getting high performance and easy deployment options[1][2][3].

https://github.com/NVIDIA/NeMo

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#jupyter_notebook #ai #artificial_intelligence #chatgpt #deep_learning #from_scratch #gpt #language_model #large_language_models #llm #machine_learning #python #pytorch #transformer

You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4].

https://github.com/rasbt/LLMs-from-scratch

#jupyter_notebook #artificial_intelligence #book #large_language_models #llm #llms #oreilly #oreilly_books

You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs.

https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

#javascript #ai #anthropic #chatbots #chatgpt #claude #gemini #generative_ai #google_deepmind #large_language_models #llm #openai #prompt_engineering #prompt_injection #prompts

There is a collection of system prompts used by popular chatbots like ChatGPT and others. These prompts are instructions that guide how chatbots respond. They are now available publicly on GitHub, which can be very helpful for users. By seeing these prompts, users can understand how chatbots work and even learn how to create their own AI tools. This can help developers build better AI applications and improve their understanding of AI technology.

https://github.com/asgeirtj/system_prompts_leaks

#jupyter_notebook #chatgpt #finance #fingpt #fintech #large_language_models #machine_learning #nlp #prompt_engineering #pytorch #reinforcement_learning #robo_advisor #sentiment_analysis #technical_analysis

FinGPT is an open-source AI tool designed specifically for finance, helping you analyze financial news, predict stock prices, and get personalized investment advice quickly and affordably. Unlike costly models like BloombergGPT, FinGPT can be updated frequently with new data at a low cost, making it more accessible and timely. It uses advanced techniques like reinforcement learning from human feedback to tailor advice to your preferences, such as risk tolerance. You can use FinGPT for tasks like sentiment analysis, robo-advising, fraud detection, and portfolio optimization, helping you make smarter financial decisions with up-to-date insights.

https://github.com/AI4Finance-Foundation/FinGPT

#python #large_language_models #machine_learning_systems #natural_language_processing

Flash Linear Attention (FLA) is a fast, memory-efficient library for advanced linear attention models used in transformers, written in PyTorch and Triton, and compatible with NVIDIA, AMD, and Intel GPUs. It offers many state-of-the-art linear attention models and fused modules that speed up training and reduce memory use. You can easily replace standard attention layers in your models with FLA’s efficient versions, improving training and inference speed, especially for long sequences. FLA supports hybrid models mixing linear and standard attention, and integrates with Hugging Face Transformers for easy use and evaluation. This helps you train and run large language models faster and with less memory, making your AI projects more efficient and scalable.

https://github.com/fla-org/flash-linear-attention

#cplusplus #automatic_differentiation #large_language_models #machine_learning #tensor_algebra

GGML is a lightweight, efficient tensor library written in C that helps you run large machine learning models on everyday hardware like laptops, phones, and even Raspberry Pi. It supports integer quantization (reducing model size and speeding up processing), automatic differentiation, and works across many platforms without needing extra software. GGML uses zero memory allocation during runtime, which improves performance and is great for edge devices with limited resources. You can build and run models easily, including GPT-2, and it supports CUDA, Android, and other hardware. This means you can use advanced AI models faster and cheaper on your existing devices.

https://github.com/ggml-org/ggml

#python #brain_inspired_ai #deep_learning #large_language_models #reasoning

The Hierarchical Reasoning Model (HRM) is a new type of AI that reasons more like a human brain, using a fast part for quick details and a slow part for big-picture planning. It solves hard logic tasks like Sudoku, mazes, and IQ-style puzzles very well, even though it is tiny (only 27 million parameters) and learns from very little data (just 1,000 examples). Unlike most large language models, it does not need long chains of written reasoning steps or huge amounts of training, which makes it much faster, cheaper, and more efficient. For the user, this means powerful reasoning in a small, fast system that can run on ordinary hardware and still beat much larger models on tough problems.

https://github.com/sapientinc/HRM

#python #large_language_models #llm #penetration_testing #python

PentestGPT is a free, open-source AI tool that automates penetration testing like solving CTF challenges in web, crypto, and more. Install easily with Docker, add your API key (Anthropic, OpenAI, or local LLMs), then run pentestgpt --target [IP] for interactive guidance on scans, exploits, and reports. New v1.0 adds autonomous agents and session saving. It boosts your speed and accuracy in ethical hacking, helping beginners learn steps fast and pros tackle complex targets efficiently.

https://github.com/GreyDGL/PentestGPT

#python #gemini #gemini_ai #gemini_api #gemini_flash #gemini_pro #information_extration #large_language_models #llm #nlp #python #structured_data

**LangExtract** is a free Python library that uses AI models like Gemini to pull structured data—like names, emotions, or meds—from messy text such as reports or books. It links every fact to its exact spot in the original, creates interactive visuals for easy checks, handles huge files fast with chunking and parallel runs, and works with cloud or local models without fine-tuning. You benefit by quickly turning unstructured docs into reliable, organized data for analysis, saving time and boosting accuracy in fields like healthcare or research.

https://github.com/google/langextract