XiaoXinAir14IML_2019_hackintosh is a repository dedicated to enabling macOS installation on Lenovo XiaoXin Air-14 IML 2019 laptops. The repository provides detailed information on the hardware specifications, supported systems, BIOS versions, related models, installation methods, updates, patches, and recommended settings. It also includes tools and guides for BIOS modifications, enabling high-resolution display settings, Bluetooth synchronization between macOS and Windows 10, voltage adjustments for efficiency, and experimental support for YogaSMC. The repository offers solutions for various issues like sleep support, sound card emulation, and battery information. It acknowledges the contributions of developers and tools like OpenCore, itlwm, VoodooI2C, and ALCPlugFix.

The daily_stock_analysis repository is an intelligent stock analysis system based on AI large models for A-share/Hong Kong stock/US stock selection. It automatically analyzes and pushes a 'decision dashboard' to WeChat Work/Feishu/Telegram/email daily. The system features multi-dimensional analysis, global market support, market review, AI backtesting validation, multi-channel notifications, and scheduled execution using GitHub Actions. It utilizes AI models like Gemini, OpenAI, DeepSeek, and data sources like AkShare, Tushare, Pytdx, Baostock, YFinance for analysis. The system includes built-in trading disciplines like risk warning, trend trading, precise entry/exit points, and checklist marking for conditions.

Jimeng AI Free API is a reverse-engineered API server that encapsulates Jimeng AI's image and video generation capabilities into OpenAI-compatible API interfaces. It supports the latest jimeng-5.0-preview, jimeng-4.6 text-to-image models, Seedance 2.0 multi-image intelligent video generation, zero-configuration deployment, and multi-token support. The API is fully compatible with OpenAI API format, seamlessly integrating with existing clients and supporting multiple session IDs for polling usage.

lingti-bot is an AI Bot platform that integrates MCP Server, multi-platform message gateway, rich toolset, intelligent conversation, and voice interaction. It offers core advantages like zero-dependency deployment with a single 30MB binary file, cloud relay support for quick integration with enterprise WeChat/WeChat Official Account, built-in browser automation with CDP protocol control, 75+ MCP tools covering various scenarios, native support for Chinese platforms like DingTalk, Feishu, enterprise WeChat, WeChat Official Account, and more. It is embeddable, supports multiple AI backends like Claude, DeepSeek, Kimi, MiniMax, and Gemini, and allows access from platforms like DingTalk, Feishu, enterprise WeChat, WeChat Official Account, Slack, Telegram, and Discord. The bot is designed with simplicity as the highest design principle, focusing on zero-dependency deployment, embeddability, plain text output, code restraint, and cloud relay support.

The GPT Server project leverages the basic capabilities of FastChat to provide the capabilities of an openai server. It perfectly adapts more models, optimizes models with poor compatibility in FastChat, and supports loading vllm, LMDeploy, and hf in various ways. It also supports all sentence_transformers compatible semantic vector models, including Chat templates with function roles, Function Calling (Tools) capability, and multi-modal large models. The project aims to reduce the difficulty of model adaptation and project usage, making it easier to deploy the latest models with minimal code changes.

Peter Cat is an intelligent Q&A chatbot solution designed for community maintainers and developers. It provides a conversational Q&A agent configuration system, self-hosting deployment solutions, and a convenient integrated application SDK. Users can easily create intelligent Q&A chatbots for their GitHub repositories and quickly integrate them into various official websites or projects to provide more efficient technical support for the community.

MedicalGPT is a training medical GPT model with ChatGPT training pipeline, implement of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(Direct Preference Optimization).

AgentKit Samples is a repository containing a series of examples and tutorials to help users understand, implement, and integrate various functionalities of AgentKit into their applications. The platform offers a complete solution for building, deploying, and maintaining AI agents, significantly reducing the complexity of developing intelligent applications. The repository provides different levels of examples and tutorials, including basic tutorials for understanding AgentKit's concepts and use cases, as well as more complex examples for experienced developers.

The 'ai-app' repository is a comprehensive collection of tools and resources related to artificial intelligence, focusing on topics such as server environment setup, PyCharm and Anaconda installation, large model deployment and training, Transformer principles, RAG technology, vector databases, AI image, voice, and music generation, and AI Agent frameworks. It also includes practical guides and tutorials on implementing various AI applications. The repository serves as a valuable resource for individuals interested in exploring different aspects of AI technology.

MiniCPM is a series of open-source large models on the client side jointly developed by Face Intelligence and Tsinghua University Natural Language Processing Laboratory. The main language model MiniCPM-2B has only 2.4 billion (2.4B) non-word embedding parameters, with a total of 2.7B parameters. - After SFT, MiniCPM-2B performs similarly to Mistral-7B on public comprehensive evaluation sets (better in Chinese, mathematics, and code capabilities), and outperforms models such as Llama2-13B, MPT-30B, and Falcon-40B overall. - After DPO, MiniCPM-2B also surpasses many representative open-source large models such as Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, and Zephyr-7B-alpha on the current evaluation set MTBench, which is closest to the user experience. - Based on MiniCPM-2B, a multi-modal large model MiniCPM-V 2.0 on the client side is constructed, which achieves the best performance of models below 7B in multiple test benchmarks, and surpasses larger parameter scale models such as Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on the OpenCompass leaderboard. MiniCPM-V 2.0 also demonstrates leading OCR capabilities, approaching Gemini Pro in scene text recognition capabilities. - After Int4 quantization, MiniCPM can be deployed and inferred on mobile phones, with a streaming output speed slightly higher than human speech speed. MiniCPM-V also directly runs through the deployment of multi-modal large models on mobile phones. - A single 1080/2080 can efficiently fine-tune parameters, and a single 3090/4090 can fully fine-tune parameters. A single machine can continuously train MiniCPM, and the secondary development cost is relatively low.

This repository provides a detailed tutorial on how to build your own large language model (LLM) from scratch. It includes all the code necessary to create a GPT-like LLM, covering the encoding, pre-training, and fine-tuning processes. The tutorial is written in a clear and concise style, with plenty of examples and illustrations to help you understand the concepts involved. It is suitable for developers and researchers with some programming experience who are interested in learning more about LLMs and how to build them.

LLMIO is a Go-based LLM load balancing gateway that provides a unified REST API, weight scheduling, logging, and modern management interface for your LLM clients. It helps integrate different model capabilities from OpenAI, Anthropic, Gemini, and more in a single service. Features include unified API compatibility, weight scheduling with two strategies, visual management dashboard, rate and failure handling, and local persistence with SQLite. The tool supports multiple vendors' APIs and authentication methods, making it versatile for various AI model integrations.

DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.

Llama中文社区是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。 **已经基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级【Done】**。**正在对Llama3模型进行中文能力的持续迭代升级【Doing】** 我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。

Feishu-MCP is a server that provides access, editing, and structured processing capabilities for Feishu documents for Cursor, Windsurf, Cline, and other AI-driven coding tools, based on the Model Context Protocol server. This project enables AI coding tools to directly access and understand the structured content of Feishu documents, significantly improving the intelligence and efficiency of document processing. It covers the real usage process of Feishu documents, allowing efficient utilization of document resources, including folder directory retrieval, content retrieval and understanding, smart creation and editing, efficient search and retrieval, and more. It enhances the intelligent access, editing, and searching of Feishu documents in daily usage, improving content processing efficiency and experience.

AstrBot is a powerful and versatile tool that leverages the capabilities of large language models (LLMs) like GPT-3, GPT-3.5, and GPT-4 to enhance communication and automate tasks. It seamlessly integrates with popular messaging platforms such as QQ, QQ Channel, and Telegram, enabling users to harness the power of AI within their daily conversations and workflows.

XiaoXinAir14IML_2019_hackintosh is a repository dedicated to enabling macOS installation on Lenovo XiaoXin Air-14 IML 2019 laptops. The repository provides detailed information on the hardware specifications, supported systems, BIOS versions, related models, installation methods, updates, patches, and recommended settings. It also includes tools and guides for BIOS modifications, enabling high-resolution display settings, Bluetooth synchronization between macOS and Windows 10, voltage adjustments for efficiency, and experimental support for YogaSMC. The repository offers solutions for various issues like sleep support, sound card emulation, and battery information. It acknowledges the contributions of developers and tools like OpenCore, itlwm, VoodooI2C, and ALCPlugFix.

TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

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AIOsense is an all-in-one sensor that is modular, affordable, and easy to solder. It is designed to be an alternative to commercially available sensors and focuses on upgradeability. AIOsense is cheaper and better than most commercial sensors and supports a variety of sensors and modules, including: - (RGB)-LED - Barometer - Breath VOC equivalent - Buzzer / Beeper - CO² equivalent - Humidity sensor - Light / Illumination sensor - PIR motion sensor - Temperature sensor - mmWave / Radar sensor Upcoming features include full voice assistant support, microphone, and speaker. All supported sensors & modules are listed in the documentation. AIOsense has a low power consumption, with an idle power consumption of 0.45W / 0.09A on a fully equipped board. Without a mmWave sensor, the idle power consumption is around 0.11W / 0.02A. To get started with AIOsense, you can refer to the documentation. If you have any questions, you can open an issue.

The IBM Analog Hardware Acceleration Kit is an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. It consists of two main components: Pytorch integration and Analog devices simulator. The Pytorch integration provides a series of primitives and features that allow using the toolkit within PyTorch, including analog neural network modules, analog training using torch training workflow, and analog inference using torch inference workflow. The Analog devices simulator is a high-performant (CUDA-capable) C++ simulator that allows for simulating a wide range of analog devices and crossbar configurations by using abstract functional models of material characteristics with adjustable parameters. Along with the two main components, the toolkit includes other functionalities such as a library of device presets, a module for executing high-level use cases, a utility to automatically convert a downloaded model to its equivalent Analog model, and integration with the AIHW Composer platform. The toolkit is currently in beta and under active development, and users are advised to be mindful of potential issues and keep an eye for improvements, new features, and bug fixes in upcoming versions.

ByteMLPerf is an AI Accelerator Benchmark that focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware. Byte MLPerf has the following characteristics: - Models and runtime environments are more closely aligned with practical business use cases. - For ASIC hardware evaluation, besides evaluate performance and accuracy, it also measure metrics like compiler usability and coverage. - Performance and accuracy results obtained from testing on the open Model Zoo serve as reference metrics for evaluating ASIC hardware integration.

This project aims to create a real-life version of GLaDOS, an aware, interactive, and embodied AI entity. It involves training a voice generator, developing a 'Personality Core,' implementing a memory system, providing vision capabilities, creating 3D-printable parts, and designing an animatronics system. The software architecture focuses on low-latency voice interactions, utilizing a circular buffer for data recording, text streaming for quick transcription, and a text-to-speech system. The project also emphasizes minimal dependencies for running on constrained hardware. The hardware system includes servo- and stepper-motors, 3D-printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions cover setting up the TTS engine, required Python packages, compiling llama.cpp, installing an inference backend, and voice recognition setup. GLaDOS can be run using 'python glados.py' and tested using 'demo.ipynb'.

OpenGlass is an open-source project that allows users to transform any regular glasses into smart glasses using affordable off-the-shelf components. With a cost of less than $25, users can enhance their glasses to record their daily activities, recognize people, identify objects, translate text, and more. The project provides detailed instructions on hardware setup and software installation, making it accessible for DIY enthusiasts and tech enthusiasts alike. By following the steps outlined in the repository, users can create their own smart glasses and explore various functionalities offered by the project.