The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

ScaleLLM is a cutting-edge inference system engineered for large language models (LLMs), meticulously designed to meet the demands of production environments. It extends its support to a wide range of popular open-source models, including Llama3, Gemma, Bloom, GPT-NeoX, and more. ScaleLLM is currently undergoing active development. We are fully committed to consistently enhancing its efficiency while also incorporating additional features. Feel free to explore our **_Roadmap_** for more details. ## Key Features * High Efficiency: Excels in high-performance LLM inference, leveraging state-of-the-art techniques and technologies like Flash Attention, Paged Attention, Continuous batching, and more. * Tensor Parallelism: Utilizes tensor parallelism for efficient model execution. * OpenAI-compatible API: An efficient golang rest api server that compatible with OpenAI. * Huggingface models: Seamless integration with most popular HF models, supporting safetensors. * Customizable: Offers flexibility for customization to meet your specific needs, and provides an easy way to add new models. * Production Ready: Engineered with production environments in mind, ScaleLLM is equipped with robust system monitoring and management features to ensure a seamless deployment experience.

Open-Lyrics is a Python library that transcribes voice files using faster-whisper and translates/polishes the resulting text into `.lrc` files in the desired language using LLM, e.g. OpenAI-GPT, Anthropic-Claude. It offers well preprocessed audio to reduce hallucination and context-aware translation to improve translation quality. Users can install the library from PyPI or GitHub and follow the installation steps to set up the environment. The tool supports GUI usage and provides Python code examples for transcription and translation tasks. It also includes features like utilizing context and glossary for translation enhancement, pricing information for different models, and a list of todo tasks for future improvements.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.

OptScale is an open-source FinOps and MLOps platform that provides cloud cost optimization for all types of organizations and MLOps capabilities like experiment tracking, model versioning, ML leaderboards.

IMAGINE - AI is a groundbreaking image generator tool that leverages the power of OpenAI's DALL-E 2 API library to create extraordinary visuals. Developed using Node.js and Express, this tool offers a transformative way to unleash artistic creativity and imagination by generating unique and captivating images through simple prompts or keywords.

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

PromptClip is a tool that allows developers to create video clips using LLM prompts. Users can upload videos from various sources, prompt the video in natural language, use different LLM models, instantly watch the generated clips, finetune the clips, and add music or image overlays. The tool provides a seamless way to extract specific moments from videos based on user queries, making video editing and content creation more efficient and intuitive.

AiScript is a lightweight scripting language that runs on JavaScript. It supports arrays, objects, and functions as first-class citizens, and is easy to write without the need for semicolons or commas. AiScript runs in a secure sandbox environment, preventing infinite loops from freezing the host. It also allows for easy provision of variables and functions from the host.

LangChain Rust is a library for building applications with Large Language Models (LLMs) through composability. It provides a set of tools and components that can be used to create conversational agents, document loaders, and other applications that leverage LLMs. LangChain Rust supports a variety of LLMs, including OpenAI, Azure OpenAI, Ollama, and Anthropic Claude. It also supports a variety of embeddings, vector stores, and document loaders. LangChain Rust is designed to be easy to use and extensible, making it a great choice for developers who want to build applications with LLMs.

LLMFarm_core.swift is a Swift library designed to work with large language models (LLM). It enables users to load different LLMs with specific parameters. The library supports MacOS (13+) and iOS (16+), offering various inferences and sampling methods. It includes features such as Metal support (not compatible with Intel Mac), model setting templates, LoRA adapters support, and LoRA train support. The library is based on ggml and llama.cpp by Georgi Gerganov, with additional sources from rwkv.cpp by saharNooby and Mia by byroneverson.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

LongLLaVA is a tool for scaling multi-modal LLMs to 1000 images efficiently via hybrid architecture. It includes stages for single-image alignment, instruction-tuning, and multi-image instruction-tuning, with evaluation through a command line interface and model inference. The tool aims to achieve GPT-4V level capabilities and beyond, providing reproducibility of results and benchmarks for efficiency and performance.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

This repository contains code for building cooperative embodied agents modularly with large language models. The agents are trained to perform tasks in two different environments: ThreeDWorld Multi-Agent Transport (TDW-MAT) and Communicative Watch-And-Help (C-WAH). TDW-MAT is a multi-agent environment where agents must transport objects to a goal position using containers. C-WAH is an extension of the Watch-And-Help challenge, which enables agents to send messages to each other. The code in this repository can be used to train agents to perform tasks in both of these environments.

GPT4Point is a unified framework for point-language understanding and generation. It aligns 3D point clouds with language, providing a comprehensive solution for tasks such as 3D captioning and controlled 3D generation. The project includes an automated point-language dataset annotation engine, a novel object-level point cloud benchmark, and a 3D multi-modality model. Users can train and evaluate models using the provided code and datasets, with a focus on improving models' understanding capabilities and facilitating the generation of 3D objects.

The ASReview project implements active learning for systematic reviews, utilizing AI-aided pipelines to assist in finding relevant texts for search tasks. It accelerates the screening of textual data with minimal human input, saving time and increasing output quality. The software offers three modes: Oracle for interactive screening, Exploration for teaching purposes, and Simulation for evaluating active learning models. ASReview LAB is designed to support decision-making in any discipline or industry by improving efficiency and transparency in screening large amounts of textual data.

Groma is a grounded multimodal assistant that excels in region understanding and visual grounding. It can process user-defined region inputs and generate contextually grounded long-form responses. The tool presents a unique paradigm for multimodal large language models, focusing on visual tokenization for localization. Groma achieves state-of-the-art performance in referring expression comprehension benchmarks. The tool provides pretrained model weights and instructions for data preparation, training, inference, and evaluation. Users can customize training by starting from intermediate checkpoints. Groma is designed to handle tasks related to detection pretraining, alignment pretraining, instruction finetuning, instruction following, and more.

Amber is the first model in the LLM360 family, an initiative for comprehensive and fully open-sourced LLMs. It is a 7B English language model with the LLaMA architecture. The model type is a language model with the same architecture as LLaMA-7B. It is licensed under Apache 2.0. The resources available include training code, data preparation, metrics, and fully processed Amber pretraining data. The model has been trained on various datasets like Arxiv, Book, C4, Refined-Web, StarCoder, StackExchange, and Wikipedia. The hyperparameters include a total of 6.7B parameters, hidden size of 4096, intermediate size of 11008, 32 attention heads, 32 hidden layers, RMSNorm ε of 1e^-6, max sequence length of 2048, and a vocabulary size of 32000.

LLM-SFT is a Chinese large model fine-tuning tool that supports models such as ChatGLM, LlaMA, Bloom, Baichuan-7B, and frameworks like LoRA, QLoRA, DeepSpeed, UI, and TensorboardX. It facilitates tasks like fine-tuning, inference, evaluation, and API integration. The tool provides pre-trained weights for various models and datasets for Chinese language processing. It requires specific versions of libraries like transformers and torch for different functionalities.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.