#jupyter_notebook #computer_vision #deep_learning #drug_discovery #forecasting #large_language_models #mxnet #nlp #paddlepaddle #pytorch #recommender_systems #speech_recognition #speech_synthesis #tensorflow #tensorflow2 #translation

This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.

https://github.com/NVIDIA/DeepLearningExamples

#python #emnlp2024 #knowledge_curation #large_language_models #naacl #nlp #report_generation #retrieval_augmented_generation

STORM is a system that helps you write articles like those on Wikipedia by using internet searches. Here’s how it benefits you STORM conducts internet research, collects references, and generates an outline for your topic.
- **Collaborative Feature** You can install STORM using `pip install knowledge-storm` and customize it according to your needs.
- **User-Friendly**: Over 70,000 people have used STORM, and it helps experienced Wikipedia editors in their pre-writing stage.

This system makes researching and writing articles much easier and more efficient.

https://github.com/stanford-oval/storm

#python #agent #ai #chatglm #fine_tuning #gpt #instruction_tuning #language_model #large_language_models #llama #llama3 #llm #lora #mistral #moe #peft #qlora #quantization #qwen #rlhf #transformers

LLaMA Factory is a tool that makes it easy to fine-tune large language models. It supports many different models like LLaMA, ChatGLM, and Qwen, among others. You can use various training methods such as full-tuning, freeze-tuning, LoRA, and QLoRA, which are efficient and save GPU memory. The tool also includes advanced algorithms and practical tricks to improve performance.

Using LLaMA Factory, you can train models up to 3.7 times faster with better results compared to other methods. It provides a user-friendly interface through Colab, PAI-DSW, or local machines, and even offers a web UI for easier management. The benefit to you is that it simplifies the process of fine-tuning large language models, making it faster and more efficient, which can be very useful for research and development projects.

https://github.com/hiyouga/LLaMA-Factory

#python #chatgpt #generative_ai #large_language_models #react_flow

Langflow is a tool that helps you build AI applications easily, even if you're not an expert. It's based on Python and works with any model, API, or database. You can use a visual interface to drag and drop elements to build your application, test it immediately, and manage conversations between multiple agents. Langflow offers a free cloud service so you can start quickly without any setup, and it also provides enterprise-grade security and scalability. This makes it easy to create and deploy AI applications, saving you time and effort.

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

#python #chinese #flash_attention #large_language_models #llm #natural_language_processing #pretrained_models

The Qwen series includes powerful language models and chat models that can be used for various tasks such as chatting, content creation, information extraction, summarization, translation, coding, and more. Here are the key benefits and features Qwen offers base language models (Qwen-1.8B, Qwen-7B, Qwen-14B, Qwen-72B) and chat models (Qwen-1.8B-Chat, Qwen-7B-Chat, Qwen-14B-Chat, Qwen-72B-Chat) with different sizes and capabilities.
- **Performance** The models are available in quantized forms (Int4 and Int8) which reduce memory usage and improve inference speed without significant performance degradation.
- **System Prompt** The models can use tools, act as agents, or even interpret code, with good performance on code execution and tool-use benchmarks.
- **Long-Context Understanding** Easy deployment options include using vLLM, FastChat, Web UI demos, CLI demos, and OpenAI-style APIs.
- **Finetuning**: Scripts are provided for finetuning the models using full-parameter, LoRA, and Q-LoRA methods.

Overall, Qwen models offer robust performance, flexibility, and ease of use, making them suitable for a wide range of applications.

https://github.com/QwenLM/Qwen

#python #auto_regressive_model #autoregressive_models #diffusion_models #generative_ai #generative_model #gpt #gpt_2 #image_generation #large_language_models #neurips #transformers #vision_transformer

VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.

https://github.com/FoundationVision/VAR

#typescript #agent_monitoring #analytics #evaluation #gpt #langchain #large_language_models #llama_index #llm #llm_cost #llm_evaluation #llm_observability #llmops #monitoring #open_source #openai #playground #prompt_engineering #prompt_management #ycombinator

Helicone is an all-in-one, open-source platform for developing and managing Large Language Models (LLMs). It allows you to integrate with various LLM providers like OpenAI, Anthropic, and more with just one line of code. You can observe and debug your model's performance, analyze metrics such as cost and latency, and fine-tune your models easily. The platform also offers a playground to test and iterate on prompts and sessions, and it supports prompt management and automatic evaluations. Helicone is enterprise-ready, compliant with SOC 2 and GDPR, and offers a generous free tier of 100k requests per month. This makes it easier to manage and optimize your LLM projects efficiently.

https://github.com/Helicone/helicone

#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