instructlab

instructlab

创新的大语言模型对齐调优命令行工具

InstructLab是为大语言模型(LLM)对齐调优设计的创新命令行工具。它采用合成数据方法,支持预训练模型下载、知识技能添加、合成数据生成、模型重训练和评估。工具兼容多种硬件平台,包括Apple M系列、AMD ROCm和NVIDIA CUDA,为LLM优化提供灵活高效的解决方案。

InstructLabCLILLM训练模型聊天机器人Github开源项目

InstructLab 🐶 (ilab)

Lint Tests Build Release License

📖 Contents

Welcome to the InstructLab CLI

InstructLab 🐶 uses a novel synthetic data-based alignment tuning method for Large Language Models (LLMs.) The "lab" in InstructLab 🐶 stands for Large-Scale Alignment for ChatBots [1].

[1] Shivchander Sudalairaj*, Abhishek Bhandwaldar*, Aldo Pareja*, Kai Xu, David D. Cox, Akash Srivastava*. "LAB: Large-Scale Alignment for ChatBots", arXiv preprint arXiv: 2403.01081, 2024. (* denotes equal contributions)

🎺 What's new

InstructLab release 0.17.0 on June 14, 2024 contains updates to the ilab CLI design. The ilab commands now fall into groups for an easier workflow and understanding of the commands. For more information, see the InstructLab CLI reference To view all the available flags for each command group, use the --help tag after the command. The original commands are still in effect, but will be deprecated in release 0.19.0 on July 11, 2024.

❓ What is ilab

ilab is a Command-Line Interface (CLI) tool that allows you to perform the following actions:

  1. Download a pre-trained Large Language Model (LLM).
  2. Chat with the LLM.

To add new knowledge and skills to the pre-trained LLM, add information to the companion taxonomy repository.

After you have added knowledge and skills to the taxonomy, you can perform the following actions:

  1. Use ilab to generate new synthetic training data based on the changes in your local taxonomy repository.
  2. Re-train the LLM with the new training data.
  3. Chat with the re-trained LLM to see the results.
graph TD; download-->chat chat[Chat with the LLM]-->add add[Add new knowledge\nor skill to taxonomy]-->generate[generate new\nsynthetic training data] generate-->train train[Re-train]-->|Chat with\nthe re-trained LLM\nto see the results|chat

For an overview of the full workflow, see the workflow diagram.

[!IMPORTANT] We have optimized InstructLab so that community members with commodity hardware can perform these steps. However, running InstructLab on a laptop will provide a low-fidelity approximation of synthetic data generation (using the ilab data generate command) and model instruction tuning (using the ilab model train command, which uses QLoRA). To achieve higher quality, use more sophisticated hardware and configure InstructLab to use a larger teacher model such as Mixtral.

📋 Requirements

  • 🍎 Apple M1/M2/M3 Mac or 🐧 Linux system (tested on Fedora). We anticipate support for more operating systems in the future.
  • C++ compiler
  • Python 3.10 or Python 3.11
  • Approximately 60GB disk space (entire process)

NOTE: Python 3.12 is currently not supported, because some dependencies don't work on Python 3.12, yet.

<!-- -->

NOTE: When installing the ilab CLI on macOS, you may have to run the xcode-select --install command, installing the required packages previously listed.

✅ Getting started

🧰 Installing ilab

  1. When installing on Fedora Linux, install C++, Python 3.10 or 3.11, and other necessary tools by running the following command:

    sudo dnf install gcc gcc-c++ make git python3.11 python3.11-devel

    If you are running on macOS, this installation is not necessary and you can begin your process with the following step.

  2. Create a new directory called instructlab to store the files the ilab CLI needs when running and cd into the directory by running the following command:

    mkdir instructlab cd instructlab

    NOTE: The following steps in this document use Python venv for virtual environments. However, if you use another tool such as pyenv or Conda Miniforge for managing Python environments on your machine continue to use that tool instead. Otherwise, you may have issues with packages that are installed but not found in venv.

  3. There are a few ways you can locally install the ilab CLI. Select your preferred installation method from the following instructions. You can then install ilab and activate your venv environment.

    NOTE: ⏳ pip install may take some time, depending on your internet connection. In case installation fails with error unsupported instruction `vpdpbusd', append -C cmake.args="-DLLAMA_NATIVE=off" to pip install command.

    See the GPU acceleration documentation for how to to enable hardware acceleration for interaction and training on AMD ROCm, Apple Metal Performance Shaders (MPS), and Nvidia CUDA.

    Install using PyTorch without CUDA bindings and no GPU acceleration

    python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install 'instructlab[cpu]' \ --extra-index-url=https://download.pytorch.org/whl/cpu \ -C cmake.args="-DLLAMA_NATIVE=off"

    NOTE: Additional Build Argument for Intel Macs

    If you have an Mac with an Intel CPU, you must add a prefix of CMAKE_ARGS="-DLLAMA_METAL=off" to the pip install command to ensure that the build is done without Apple M-series GPU support.

    (venv) $ CMAKE_ARGS="-DLLAMA_METAL=off" pip install ...

    Install with AMD ROCm

    python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install 'instructlab[rocm]' \ --extra-index-url https://download.pytorch.org/whl/rocm6.0 \ -C cmake.args="-DLLAMA_HIPBLAS=on" \ -C cmake.args="-DAMDGPU_TARGETS=all" \ -C cmake.args="-DCMAKE_C_COMPILER=/opt/rocm/llvm/bin/clang" \ -C cmake.args="-DCMAKE_CXX_COMPILER=/opt/rocm/llvm/bin/clang++" \ -C cmake.args="-DCMAKE_PREFIX_PATH=/opt/rocm" \ -C cmake.args="-DLLAMA_NATIVE=off"

    On Fedora 40+, use -DCMAKE_C_COMPILER=clang-17 and -DCMAKE_CXX_COMPILER=clang++-17.

    Install with Apple Metal on M1/M2/M3 Macs

    NOTE: Make sure your system Python build is Mach-O 64-bit executable arm64 by using file -b $(command -v python), or if your system is setup with pyenv by using the file -b $(pyenv which python) command.

    python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install 'instructlab[mps]'

    Install with Nvidia CUDA

    python3 -m venv --upgrade-deps venv source venv/bin/activate pip cache remove llama_cpp_python pip install 'instructlab[cuda]' \ -C cmake.args="-DLLAMA_CUDA=on" \ -C cmake.args="-DLLAMA_NATIVE=off"
  4. From your venv environment, verify ilab is installed correctly, by running the ilab command.

    ilab

    Example output of the ilab command

    (venv) $ ilab Usage: ilab [OPTIONS] COMMAND [ARGS]... CLI for interacting with InstructLab. If this is your first time running InstructLab, it's best to start with `ilab config init` to create the environment. Options: --config PATH Path to a configuration file. [default: config.yaml] --version Show the version and exit. --help Show this message and exit. Command: config Command group for Interacting with the Config of InstructLab data Command group for Interacting with the Data of generated by... model Command group for Interacting with the Models in InstructLab sysinfo Print system information taxonomy Command group for Interacting with the Taxonomy in InstructLab Aliases: chat: model chat convert: model convert diff: taxonomy diff download: model download generate: data generate init: config init serve: model serve test: model test train: model train

    IMPORTANT Every ilab command needs to be run from within your Python virtual environment. You can enter the Python environment by running the source venv/bin/activate command.

  5. Optional: You can enable tab completion for the ilab command.

    Bash (version 4.4 or newer)

    Enable tab completion in bash with the following command:

    eval "$(_ILAB_COMPLETE=bash_source ilab)"

    To have this enabled automatically every time you open a new shell, you can save the completion script and source it from ~/.bashrc:

    _ILAB_COMPLETE=bash_source ilab > ~/.ilab-complete.bash echo ". ~/.ilab-complete.bash" >> ~/.bashrc

    Zsh

    Enable tab completion in zsh with the following command:

    eval "$(_ILAB_COMPLETE=zsh_source ilab)"

    To have this enabled automatically every time you open a new shell, you can save the completion script and source it from ~/.zshrc:

    _ILAB_COMPLETE=zsh_source ilab > ~/.ilab-complete.zsh echo ". ~/.ilab-complete.zsh" >> ~/.zshrc

    Fish

    Enable tab completion in fish with the following command:

    _ILAB_COMPLETE=fish_source ilab | source

    To have this enabled automatically every time you open a new shell, you can save the completion script and source it from ~/.bashrc:

    _ILAB_COMPLETE=fish_source ilab > ~/.config/fish/completions/ilab.fish

🏗️ Initialize ilab

  1. Initialize ilab by running the following command:

    ilab config init

    Example output

    Welcome to InstructLab CLI. This guide will help you set up your environment. Please provide the following values to initiate the environment [press Enter for defaults]: Path to taxonomy repo [taxonomy]: <ENTER>
  2. When prompted by the interface, press Enter to add a new default config.yaml file.

  3. When prompted, clone the https://github.com/instructlab/taxonomy.git repository into the current directory by typing y.

    Optional: If you want to point to an existing local clone of the taxonomy repository, you can pass the path interactively or alternatively with the --taxonomy-path flag.

    Example output after initializing ilab

    (venv) $ ilab config init Welcome to InstructLab CLI. This guide will help you set up your environment. Please provide the following values to initiate the environment [press Enter for defaults]: Path to taxonomy repo [taxonomy]: <ENTER> `taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y Cloning https://github.com/instructlab/taxonomy.git... Generating `config.yaml` in the current directory... Initialization completed successfully, you're ready to start using `ilab`. Enjoy!

    ilab will use the default configuration file unless otherwise specified. You can override this behavior with the --config parameter for any ilab command.

📥 Download the model

  • Run the ilab model download command.

    ilab model download

    ilab model download downloads a compact pre-trained version of the model (~4.4G) from HuggingFace:

    (venv) $ ilab model download Downloading model from Hugging Face: instructlab/merlinite-7b-lab-GGUF@main to /home/user/.cache/instructlab/models... ... INFO 2024-08-01 15:05:48,464 huggingface_hub.file_download:1893: Download complete. Moving file to /home/user/.cache/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf

    NOTE ⏳ This command can take few minutes or immediately depending on your internet connection or model is cached. If you have issues connecting to Hugging Face, refer to the Hugging Face discussion forum for more details.

    Downloading a specific model from a Hugging Face repository

  • Specify repository, model, and a Hugging Face token if necessary. More information about Hugging Face tokens can be found here

    HF_TOKEN=<YOUR HUGGINGFACE TOKEN GOES HERE> ilab model download

编辑推荐精选

蛙蛙写作

蛙蛙写作

AI小说写作助手,一站式润色、改写、扩写

蛙蛙写作—国内先进的AI写作平台,涵盖小说、学术、社交媒体等多场景。提供续写、改写、润色等功能,助力创作者高效优化写作流程。界面简洁,功能全面,适合各类写作者提升内容品质和工作效率。

AI辅助写作AI工具蛙蛙写作AI写作工具学术助手办公助手营销助手AI助手
Trae

Trae

字节跳动发布的AI编程神器IDE

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

AI工具TraeAI IDE协作生产力转型热门
问小白

问小白

全能AI智能助手,随时解答生活与工作的多样问题

问小白,由元石科技研发的AI智能助手,快速准确地解答各种生活和工作问题,包括但不限于搜索、规划和社交互动,帮助用户在日常生活中提高效率,轻松管理个人事务。

热门AI助手AI对话AI工具聊天机器人
Transly

Transly

实时语音翻译/同声传译工具

Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。

讯飞智文

讯飞智文

一键生成PPT和Word,让学习生活更轻松

讯飞智文是一个利用 AI 技术的项目,能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定,还是学生群体的职业生涯规划、实习避坑指南,亦或是活动策划、旅游攻略等内容,它都能提供支持,帮助用户精准表达,轻松呈现各种信息。

AI办公办公工具AI工具讯飞智文AI在线生成PPTAI撰写助手多语种文档生成AI自动配图热门
讯飞星火

讯飞星火

深度推理能力全新升级,全面对标OpenAI o1

科大讯飞的星火大模型,支持语言理解、知识问答和文本创作等多功能,适用于多种文件和业务场景,提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台,涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能,能为不同需求的用户提供便捷高效的帮助,助力用户轻松获取信息、解决问题,满足多样化使用场景。

热门AI开发模型训练AI工具讯飞星火大模型智能问答内容创作多语种支持智慧生活
Spark-TTS

Spark-TTS

一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型

Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目,由多个知名机构联合参与。该项目提供了高效的 LLM(大语言模型)驱动的语音合成方案,支持语音克隆和语音创建功能,可通过命令行界面(CLI)和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数,生成高质量的语音。该项目适用于多种场景,如有声读物制作、智能语音助手开发等。

咔片PPT

咔片PPT

AI助力,做PPT更简单!

咔片是一款轻量化在线演示设计工具,借助 AI 技术,实现从内容生成到智能设计的一站式 PPT 制作服务。支持多种文档格式导入生成 PPT,提供海量模板、智能美化、素材替换等功能,适用于销售、教师、学生等各类人群,能高效制作出高品质 PPT,满足不同场景演示需求。

讯飞绘文

讯飞绘文

选题、配图、成文,一站式创作,让内容运营更高效

讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。

热门AI辅助写作AI工具讯飞绘文内容运营AI创作个性化文章多平台分发AI助手
材料星

材料星

专业的AI公文写作平台,公文写作神器

AI 材料星,专业的 AI 公文写作辅助平台,为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能,支持 30 + 文稿类型生成,助力快速完成领导讲话、工作总结、述职报告等材料,提升办公效率,是体制打工人的得力写作神器。

下拉加载更多