Projects sharing features with Doctor Dignity

ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,

Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,

This project is a comprehensive learning resource and set of demonstrations focused on large language model integration, deployment, and fine-tuning. It provides educational content and practical guides for working with artificial intelligence models. The resource includes specific tutorials and courses on adapting pre-trained models to specialized datasets using parameter-efficient fine-tuning techniques. It also provides instructional content for running quantized models on consumer hardware and building retrieval augmented generation pipelines using vector databases and document indexing.

This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de

This project is a LangChain-based framework for building retrieval-augmented generation systems, autonomous agents, and multimodal chatbots. It functions as an open-source orchestrator that connects local inference engines and online APIs to manage various large language model deployments. The system distinguishes itself by providing specialized interfaces for local knowledge bases, allowing the loading and vectorization of private documents to create context-aware assistants. It also supports multimodal capabilities, enabling the processing of both text and image inputs through vision-capabl

Project Nomad is a self-hosted survival suite and containerized offline operating environment. It provides a collection of essential tools, including a local retrieval-augmented generation system, an offline mapping server, and a local knowledge base for large language models, all designed to operate on air-gapped hardware. The system prioritizes total offline isolation to ensure telemetry-free operation. It enables private data analysis and semantic document querying through local-first vector storage and offline model execution, keeping all data on internal hardware without requiring intern

ERNIE is a development toolkit for training, fine-tuning, and deploying large language models built on the PaddlePaddle deep learning platform. It provides a comprehensive suite of core components, including an inference server for vision and language models, a training and fine-tuning toolkit, and a framework for building retrieval-augmented generation systems using private knowledge bases. The project features multimodal AI models capable of reasoning across text, images, and video to perform complex visual understanding and information extraction. It distinguishes itself through specialize

Intel XPU LLM Acceleration Library is a toolkit designed to accelerate large language model inference and finetuning on Intel CPUs, GPUs, and NPUs. It provides a distributed inference engine for scaling models across multiple accelerators, a multimodal model runtime for vision and speech tasks, and a low-bit model quantization tool for converting weights into INT4, FP8, and GGUF formats. The project features a parameter-efficient finetuning framework that enables model adaptation using QLoRA and DPO on Intel hardware. It distinguishes itself by providing specialized optimizations for Intel XP

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications. The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn di

This project is a collection of reference implementations and recipes for deploying, fine-tuning, and running inference with Llama large language models. It serves as a toolkit and implementation guide for adapting pre-trained models to specific tasks and domain-specific datasets. The repository provides frameworks for developing retrieval augmented generation pipelines to ground model responses in external data. It includes guides for executing quantized inference to reduce memory usage and increase processing speed. The toolkit covers a broad range of capabilities including parameter-effic

react-native-firebase is a modular set of libraries that integrates Firebase cloud services into cross-platform mobile applications. It serves as a native-SDK wrapper, mapping JavaScript method calls to native iOS and Android Firebase SDKs via the React Native bridge to provide a type-safe interface for mobile backend integration. The project enables connectivity to a wide array of cloud services, including user authentication and identity management, NoSQL cloud databases with real-time synchronization, and scalable cloud storage for media files. It also provides tools for sending push notif

ChatGLM-6B is a generative AI inference engine designed for local execution of transformer-based language models. It provides a comprehensive runtime environment that allows users to load and run pre-trained neural network weights directly on their own hardware, ensuring data privacy and independence from external cloud services. The project distinguishes itself through a hardware-agnostic execution backend that supports deployment across diverse environments, including standard processors, Apple Silicon, and multi-GPU configurations. It incorporates advanced optimization techniques such as w

PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language models using the PaddlePaddle framework. It provides a comprehensive suite for the entire natural language processing lifecycle, from model development to high-performance inference. The project features a standardized model zoo for loading and managing pre-trained models and tokenizers through a unified interface. It distinguishes itself with a specialized model compression framework that reduces memory footprints via weight precision conversion and lossless size optimization, alo

This project is a comprehensive technical reference and educational resource focused on the lifecycle of large language models. It provides structured learning materials that cover the foundational mechanics of transformer architectures, the mathematical principles of attention mechanisms, and the engineering practices required for modern generative artificial intelligence. The repository serves as a guide for both technical skill development and professional preparation, offering a curriculum that spans from model training and inference optimization to advanced alignment techniques. It detai

MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment

VisualGLM-6B is a multimodal large language model and vision-language system designed to process and generate text based on combined textual and visual inputs. It functions as a bilingual conversational AI capable of maintaining natural language interactions in both English and Chinese. The project utilizes quantized model weights to reduce memory requirements, enabling the deployment of the neural network on consumer-grade hardware. These compressed parameters allow for lower VRAM usage while maintaining the model's ability to analyze visual content and generate corresponding natural languag

LiteRT is a runtime and API for executing machine learning and generative AI models on mobile, desktop, and IoT hardware. It consists of an inference engine and a specialized environment for running quantized large language and diffusion models locally on edge hardware. The system includes an ahead-of-time model compiler that translates models into hardware-specific bytecode to reduce startup latency and memory overhead. It provides a unified interface for Neural Processing Units with automatic fallback routing to CPUs or GPUs when specific subgraph support is unavailable. An edge model conve

This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators. The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces pro

ncnn is a high-performance neural network inference framework designed for executing deep learning models locally on mobile and desktop hardware. It functions as a specialized engine that enables the deployment of artificial intelligence tasks directly on resource-constrained devices, eliminating the need for external network connectivity or cloud-based processing services. The framework provides a comprehensive toolset for model optimization, allowing users to convert and quantize machine learning models into specialized binary structures. By utilizing static model graph compilation and zero

MediaPipe is a cross-platform machine learning framework designed for deploying vision, audio, and text processing models across mobile, desktop, and web environments. It functions as an on-device inference engine that executes complex models locally on edge hardware, ensuring low latency and privacy without requiring a constant internet connection. The framework utilizes a graph-based pipeline orchestration system where data flows through a directed network of modular calculators to ensure synchronized and deterministic processing. It distinguishes itself through a unified runtime that provi

MiniCPM-o is a multimodal large language model designed to function as a real-time conversational assistant on edge devices. By mapping text, image, video, and audio inputs into a unified latent space, the system enables simultaneous cross-modal reasoning and full-duplex interaction. It is built as an edge-side inference engine, utilizing quantized model weights to maintain high-performance processing on consumer hardware. The system distinguishes itself through its integrated speech synthesis and voice cloning capabilities, which allow for the generation of expressive, personalized vocal out

The simplest way to run LLaMA on your local machine

TabNine is an AI-powered code completion engine that runs a deep-learning model to generate real-time code suggestions across all programming languages. It operates as an editor plugin that communicates with a backend through a JSON message-passing interface, processing code entirely on the local machine or within a private cloud to keep source code secure and private. The system provides a completion request API that accepts cursor context and returns ranked text completions, with features for configuring completion regions, prefetching files for indexing, and managing binary versions and up

Hoodie is a data synchronization framework and offline-first JavaScript backend designed to keep client-side data stores in sync with a remote backend. It provides a local-first persistence layer that allows applications to remain functional while offline by abstracting the client-side database. The system features a NoSQL backend interface that enables interaction with remote data stores without requiring custom server-side boilerplate code. This is supported by a modular driver system of pluggable backend adapters that connect the synchronization layer to various remote database providers.

PouchDB is a JavaScript NoSQL document store designed to persist JSON data within web browsers or Node.js environments. It functions as an offline-first data store that caches information on a local device and synchronizes with a remote server when connectivity is available. The database implements the CouchDB API to ensure compatibility for bidirectional data replication. This allows for the synchronization of documents between a local client and remote CouchDB servers to maintain consistency across multiple devices. The project provides capabilities for local browser data storage and remot

This library provides a framework for parameter-efficient fine-tuning, enabling the adaptation of large pretrained models by training only a small subset of parameters. It functions as a distributed model training system and optimization toolkit, designed to reduce the computational and memory requirements typically associated with full model fine-tuning. The project distinguishes itself through a suite of methods for modular adapter composition, including low-rank matrix decomposition and activation-based scaling. It supports the integration of multiple task-specific adapter modules, allowin