📚 从零开始构建大模型

Happy experimenting with MLLM and LLM models!

happy-llm 实践练习：colab 版本、pynb 格式，GPU 免费

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基于 Datawhale 开源项目 Happy-LLM，制定的一份为期 15 天的深度学习计划及对应代码。

Basic LLM recipes for a happy life

🚀 Explore Happy-LLM, a tool designed to enhance interactions with language models, offering a user-friendly experience in Chinese.

Как создать свою нейросеть

learning

LLM-happy学习

从零开始的大语言模型原理与实践教程

Here be all that maketh my LLMs (and me) happy!

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Explore Llama-index and NLP synergy in RAG applications. This repo showcases Python code for seamless vector store integration (Chroma-DB, Pinecone). Elevate your projects with LLM-powered NLP and efficient vector storage. Happy coding! 🚀

Happy LLM

初学者对HappyLLM的一次复现和学习

复现happy-llm

A hands-on reproduction of Datawhale's Happy-LLM course - building LLM from scratch

my gpt

Following the guidance of Happy-LLM, implement a tiny LLaMA2 from scratch.

https://github.com/datawhalechina/happy-llm.git

Set of AI/LLM utils used at Happy Data Studio

⭐ A collection of testimonials & LinkedIn recommendations I've received from happy clients over the last 6+ years as a freelance ML/LLM Engineer

This repository contains comprehensive notebooks, made by me, explaining how to efficiently fine tune different LLMs on custom dataset, that too from scratch. Happy Learning :)

LLM powered notes app that is smart and intuitive letting users focus on writing, letting app extract healthy information, form collections, add to calendars and 100s more. Happy time to write!

A tool powered by a large language model (LLM) to automatically generate unit tests for React components. Simplify the testing process by inputting your React component code and selecting predefined test scenarios such as happy path and edge cases. Accelerate your testing workflow and ensure code reliability with this intuitive and efficient tool.

By this point in time, if you're aren't making your own AI assistant, what are you doing? Use this as a template to build on. All the code is just a suggestion. Use other AI models to build it for you. Just feed terminal_ai_lite into whatever LLM (AI) you want and build/modify this in sections. Happy Hacking.

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an impletion of mini LLaMA2

HappyLLM学习仓库