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.

MindChat is a psychological large language model designed to help individuals relieve psychological stress and solve mental confusion, ultimately improving mental health. It aims to provide a relaxed and open conversation environment for users to build trust and understanding. MindChat offers privacy, warmth, safety, timely, and convenient conversation settings to help users overcome difficulties and challenges, achieve self-growth, and development. The tool is suitable for both work and personal life scenarios, providing comprehensive psychological support and therapeutic assistance to users while strictly protecting user privacy. It combines psychological knowledge with artificial intelligence technology to contribute to a healthier, more inclusive, and equal society.

PmHub is a smart project management system based on SpringCloud, SpringCloud Alibaba, and LLM. It aims to help students quickly grasp the architecture design and development process of microservices/distributed projects. PmHub provides a platform for students to experience the transformation from monolithic to microservices architecture, understand the pros and cons of both architectures, and prepare for job interviews. It offers popular technologies like SpringCloud-Gateway, Nacos, Sentinel, and provides high-quality code, continuous integration, product design documents, and an enterprise workflow system. PmHub is suitable for beginners and advanced learners who want to master core knowledge of microservices/distributed projects.

GPUPixel is a real-time, high-performance image and video filter library written in C++11 and based on OpenGL/ES. It incorporates a built-in beauty face filter that achieves commercial-grade beauty effects. The library is extremely easy to compile and integrate with a small size, supporting platforms including iOS, Android, Mac, Windows, and Linux. GPUPixel provides various filters like skin smoothing, whitening, face slimming, big eyes, lipstick, and blush. It supports input formats like YUV420P, RGBA, JPEG, PNG, and output formats like RGBA and YUV420P. The library's performance on devices like iPhone and Android is optimized, with low CPU usage and fast processing times. GPUPixel's lib size is compact, making it suitable for mobile and desktop applications.

**stylellm** is a text style transfer project based on large language models (llms). The project utilizes large language models to learn the writing style of specific literary works (commonly used vocabulary, sentence structure, rhetoric, character dialogue, etc.), forming a series of specific style models. Using the **stylellm** model, the learned style can be transferred to other general texts, that is: input a piece of original text, the model can rewrite it, output text with the characteristics of that style, achieving the effect of text modification,润色or style imitation.

This project open sources the **Chinese LLaMA model and the Alpaca large model fine-tuned with instructions**, to further promote the open research of large models in the Chinese NLP community. These models **extend the Chinese vocabulary based on the original LLaMA** and use Chinese data for secondary pre-training, further enhancing the basic Chinese semantic understanding ability. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, significantly improving the model's understanding and execution of instructions.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

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).

Chinese-LLaMA-Alpaca-2 is a large Chinese language model developed by Meta AI. It is based on the Llama-2 model and has been further trained on a large dataset of Chinese text. Chinese-LLaMA-Alpaca-2 can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. Here are some of the key features of Chinese-LLaMA-Alpaca-2: * It is the largest Chinese language model ever trained, with 13 billion parameters. * It is trained on a massive dataset of Chinese text, including books, news articles, and social media posts. * It can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. * It is open-source and available for anyone to use. Chinese-LLaMA-Alpaca-2 is a powerful tool that can be used to improve the performance of a wide range of natural language processing tasks. It is a valuable resource for researchers and developers working in the field of artificial intelligence.

Chinese-LLaMA-Alpaca-3 is a project based on Meta's latest release of the new generation open-source large model Llama-3. It is the third phase of the Chinese-LLaMA-Alpaca open-source large model series projects (Phase 1, Phase 2). This project open-sources the Chinese Llama-3 base model and the Chinese Llama-3-Instruct instruction fine-tuned large model. These models incrementally pre-train with a large amount of Chinese data on the basis of the original Llama-3 and further fine-tune using selected instruction data, enhancing Chinese basic semantics and instruction understanding capabilities. Compared to the second-generation related models, significant performance improvements have been achieved.

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.

PaddleNLP is an easy-to-use and high-performance NLP library. It aggregates high-quality pre-trained models in the industry and provides out-of-the-box development experience, covering a model library for multiple NLP scenarios with industry practice examples to meet developers' flexible customization needs.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

AIdea Server is an open-source Golang-based server that integrates mainstream large language models and drawing models. It supports various functionalities including OpenAI's GPT-3.5 and GPT-4, Anthropic's Claude instant and Claude 2.1, Google's Gemini Pro, as well as Chinese models like Tongyi Qianwen, Wenxin Yiyuan, and more. It also supports open-source large models like Yi 34B, Llama2, and AquilaChat 7B. Additionally, it provides features for text-to-image, super-resolution, coloring black and white images, generating art fonts and QR codes, among others.

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.

MLC LLM is a high-performance universal deployment solution that allows native deployment of any large language models with native APIs with compiler acceleration. It supports a wide range of model architectures and variants, including Llama, GPT-NeoX, GPT-J, RWKV, MiniGPT, GPTBigCode, ChatGLM, StableLM, Mistral, and Phi. MLC LLM provides multiple sets of APIs across platforms and environments, including Python API, OpenAI-compatible Rest-API, C++ API, JavaScript API and Web LLM, Swift API for iOS App, and Java API and Android App.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

The Bookworm·Puyu large model training camp aims to promote the implementation of large models in more industries and provide developers with a more efficient platform for learning the development and application of large models. Within two weeks, you will learn the entire process of fine-tuning, deploying, and evaluating large models.

This project aims to create a RESTful API server compatible with the OpenAI API using open-source backends like llama/llama2. With this project, various GPT tools/frameworks can be compatible with your own model. Key features include: - **Compatibility with OpenAI API**: The API server follows the OpenAI API structure, allowing seamless integration with existing tools and frameworks. - **Support for Multiple Backends**: The server supports both llama.cpp and pyllama backends, providing flexibility in model selection. - **Customization Options**: Users can configure model parameters such as temperature, top_p, and top_k to fine-tune the model's behavior. - **Batch Processing**: The API supports batch processing for embeddings, enabling efficient handling of multiple inputs. - **Token Authentication**: The server utilizes token authentication to secure access to the API. This tool is particularly useful for developers and researchers who want to integrate large language models into their applications or explore custom models without relying on proprietary APIs.

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.

This repository provides a comprehensive overview of large language models (LLMs), covering various aspects such as their history, types, underlying architecture, training techniques, and applications. It includes detailed explanations of key concepts like Transformer models, distributed training, fine-tuning, and reinforcement learning. The repository also discusses the evaluation and limitations of LLMs, including the phenomenon of hallucinations. Additionally, it provides a list of related courses and references for further exploration.

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

PhoGPT is an open-source 4B-parameter generative model series for Vietnamese, including the base pre-trained monolingual model PhoGPT-4B and its chat variant, PhoGPT-4B-Chat. PhoGPT-4B is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length and a vocabulary of 20K token types. PhoGPT-4B-Chat is fine-tuned on instructional prompts and conversations, demonstrating superior performance. Users can run the model with inference engines like vLLM and Text Generation Inference, and fine-tune it using llm-foundry. However, PhoGPT has limitations in reasoning, coding, and mathematics tasks, and may generate harmful or biased responses.

This repository contains projects and notes on common practical techniques for fine-tuning Large Language Models (LLMs). It includes fine-tuning LLM notebooks, Colab links, LLM techniques and utils, and other smaller language models. The repository also provides links to YouTube videos explaining the concepts and techniques discussed in the notebooks.

LLoCO is a technique that learns documents offline through context compression and in-domain parameter-efficient finetuning using LoRA, which enables LLMs to handle long context efficiently.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

LLM-baselines is a modular codebase to experiment with transformers, inspired from NanoGPT. It provides a quick and easy way to train and evaluate transformer models on a variety of datasets. The codebase is well-documented and easy to use, making it a great resource for researchers and practitioners alike.

This repository contains Jupyter-based tutorials for NLP, ML, AI in Python for classes in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. It provides a central leaderboard and submission interface, making it easier for researchers to reproduce results mentioned in papers and perform reliable & accurate quantitative analysis. EvalAI also offers features such as custom evaluation protocols and phases, remote evaluation, evaluation inside environments, CLI support, portability, and faster evaluation.

This repository provides a curated list of weekly published Large Language Model (LLM) papers. It includes top important LLM papers for each week, organized by month and year. The papers are categorized into different time periods, making it easy to find the most recent and relevant research in the field of LLM.

This project is a Chinese tutorial for domestic beginners based on the AutoDL platform, providing full-process guidance for various open-source large models, including environment configuration, local deployment, and efficient fine-tuning. It simplifies the deployment, use, and application process of open-source large models, enabling more ordinary students and researchers to better use open-source large models and helping open and free large models integrate into the lives of ordinary learners faster.