Best AI tools for< Annoy People >

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

This repository provides code for extracting image and text features using OpenAI CLIP models, supporting both global and local grid visual features. It aims to facilitate multi visual-and-language downstream tasks by allowing users to customize input and output grid resolution easily. The extracted features have shown comparable or superior results in image captioning tasks without hyperparameter tuning. The repo supports various CLIP models and provides detailed information on supported settings and results on MSCOCO image captioning. Users can get started by setting up experiments with the extracted features using X-modaler.

NeMo Guardrails is an open-source toolkit for easily adding _programmable guardrails_ to LLM-based conversational applications. Guardrails (or "rails" for short) are specific ways of controlling the output of a large language model, such as not talking about politics, responding in a particular way to specific user requests, following a predefined dialog path, using a particular language style, extracting structured data, and more.

chromem-go is an embeddable vector database for Go with a Chroma-like interface and zero third-party dependencies. It enables retrieval augmented generation (RAG) and similar embeddings-based features in Go apps without the need for a separate database. The focus is on simplicity and performance for common use cases, allowing querying of documents with minimal memory allocations. The project is in beta and may introduce breaking changes before v1.0.0.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Vicinity is a lightweight, low-dependency vector store that provides a unified interface for nearest neighbor search with support for different backends and evaluation. It simplifies the process of comparing and evaluating different nearest neighbors packages by offering a simple and intuitive API. Users can easily experiment with various indexing methods and distance metrics to choose the best one for their use case. Vicinity also allows for measuring performance metrics like queries per second and recall.

SEED-Bench is a comprehensive benchmark for evaluating the performance of multimodal large language models (LLMs) on a wide range of tasks that require both text and image understanding. It consists of two versions: SEED-Bench-1 and SEED-Bench-2. SEED-Bench-1 focuses on evaluating the spatial and temporal understanding of LLMs, while SEED-Bench-2 extends the evaluation to include text and image generation tasks. Both versions of SEED-Bench provide a diverse set of tasks that cover different aspects of multimodal understanding, making it a valuable tool for researchers and practitioners working on LLMs.

LLM4EC is an interdisciplinary research repository focusing on the intersection of Large Language Models (LLM) and Evolutionary Computation (EC). It provides a comprehensive collection of papers and resources exploring various applications, enhancements, and synergies between LLM and EC. The repository covers topics such as LLM-assisted optimization, EA-based LLM architecture search, and applications in code generation, software engineering, neural architecture search, and other generative tasks. The goal is to facilitate research and development in leveraging LLM and EC for innovative solutions in diverse domains.

Grounded-VideoLLM is a Video Large Language Model specialized in fine-grained temporal grounding. It excels in tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA. The model incorporates an additional temporal stream, discrete temporal tokens with specific time knowledge, and a multi-stage training scheme. It shows potential as a versatile video assistant for general video understanding. The repository provides pretrained weights, inference scripts, and datasets for training. Users can run inference queries to get temporal information from videos and train the model from scratch.

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