
EasyInstruct
[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.
Stars: 381

EasyInstruct is a Python package proposed as an easy-to-use instruction processing framework for Large Language Models (LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.
README:

An Easy-to-use Instruction Processing Framework for Large Language Models.
Project • Paper • Demo • Overview • Installation • Quickstart • How To Use • Docs • Video • Citation • Contributors
- 2024-06-04, EasyInstruct is accepted by ACL 2024 System Demonstration Track. 🎉🎉
- 2024-02-06 We release a new paper: "EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models" with an HF demo EasyInstruct.
- 2024-02-06 We release a preliminary tool EasyDetect for hallucination detection, with a demo.
- 2024-02-05 We release version 0.1.2, supporting for new features and optimising the function interface.
- 2023-12-09 The paper "When Do Program-of-Thoughts Work for Reasoning?" (supported by EasyInstruct), is accepted by AAAI 2024!
- 2023-10-28 We release version 0.1.1, supporting for new features of instruction generation and instruction selection.
- 2023-08-09 We release version 0.0.6, supporting Cohere API calls.
- 2023-07-12 We release EasyEdit, an easy-to-use framework to edit Large Language Models.
Previous news
- 2023-5-23 We release version 0.0.5, removing requirement of llama-cpp-python.
- 2023-5-16 We release version 0.0.4, fixing some problems.
- 2023-4-21 We release version 0.0.3, check out our documentations for more details.
- 2023-3-25 We release version 0.0.2, suporting IndexPrompt, MMPrompt, IEPrompt and more LLMs
- 2023-3-13 We release version 0.0.1, supporting in-context learning, chain-of-thought with ChatGPT.
This repository is a subproject of KnowLM.
EasyInstruct is a Python package which is proposed as an easy-to-use instruction processing framework for Large Language Models(LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.
-
The current supported instruction generation techniques are as follows:
Methods Description Self-Instruct The method that randomly samples a few instructions from a human-annotated seed tasks pool as demonstrations and prompts an LLM to generate more instructions and corresponding input-output pairs. Evol-Instruct The method that incrementally upgrades an initial set of instructions into more complex instructions by prompting an LLM with specific prompts. Backtranslation The method that creates an instruction following training instance by predicting an instruction that would be correctly answered by a portion of a document of the corpus. KG2Instruct The method that creates an instruction following training instance by predicting an instruction that would be correctly answered by a portion of a document of the corpus. -
The current supported instruction selection metrics are as follows:
Metrics Notation Description Length $Len$ The bounded length of every pair of instruction and response. Perplexity $PPL$ The exponentiated average negative log-likelihood of response. MTLD $MTLD$ Measure of textual lexical diversity, the mean length of sequential words in a text that maintains a minimum threshold TTR score. ROUGE $ROUGE$ Recall-Oriented Understudy for Gisting Evaluation, a set of metrics used for evaluating similarities between sentences. GPT score $GPT$ The score of whether the output is a good example of how AI Assistant should respond to the user's instruction, provided by ChatGPT. CIRS $CIRS$ The score using the abstract syntax tree to encode structural and logical attributes, to measure the correlation between code and reasoning abilities. -
API service providers and their corresponding LLM products that are currently available:
Model Description Default Version OpenAI GPT-3.5 A set of models that improve on GPT-3 and can understand as well as generate natural language or code. gpt-3.5-turbo
GPT-4 A set of models that improve on GPT-3.5 and can understand as well as generate natural language or code. gpt-4
Anthropic Claude A next-generation AI assistant based on Anthropic’s research into training helpful, honest, and harmless AI systems. claude-2.0
Claude-Instant A lighter, less expensive, and much faster option than Claude. claude-instant-1.2
Cohere Command A flagship text generation model of Cohere trained to follow user commands and to be instantly useful in practical business applications. command
Command-Light A light version of Command models that are faster but may produce lower-quality generated text. command-light
Installation from git repo branch:
pip install git+https://github.com/zjunlp/EasyInstruct@main
Installation for local development:
git clone https://github.com/zjunlp/EasyInstruct
cd EasyInstruct
pip install -e .
Installation using PyPI (not the latest version):
pip install easyinstruct -i https://pypi.org/simple
We provide two ways for users to quickly get started with EasyInstruct. You can either use the shell script or the Gradio app based on your specific needs.
Users can easily configure the parameters of EasyInstruct in a YAML-style file or just quickly use the default parameters in the configuration files we provide. Following is an example of the configuration file for Self-Instruct:
generator:
SelfInstructGenerator:
target_dir: data/generations/
data_format: alpaca
seed_tasks_path: data/seed_tasks.jsonl
generated_instructions_path: generated_instructions.jsonl
generated_instances_path: generated_instances.jsonl
num_instructions_to_generate: 100
engine: gpt-3.5-turbo
num_prompt_instructions: 8
More example configuration files can be found at configs.
Users should first specify the configuration file and provide their own OpenAI API key. Then, run the following shell script to launch the instruction generation or selection process.
config_file=""
openai_api_key=""
python demo/run.py \
--config $config_file\
--openai_api_key $openai_api_key \
We provide a Gradio app for users to quickly get started with EasyInstruct. You can run the following command to launch the Gradio app locally on the port 8080
(if available).
python demo/app.py
We also host a running gradio app in HuggingFace Spaces. You can try it out here.
Please refer to our documentations for more details.
The Generators
module streamlines the process of instruction data generation, allowing for the generation of instruction data based on seed data. You can choose the appropriate generator based on your specific needs.
BaseGenerator
is the base class for all generators.
You can also easily inherit this base class to customize your own generator class. Just override the
__init__
andgenerate
method.
SelfInstructGenerator
is the class for the instruction generation method of Self-Instruct. See Self-Instruct: Aligning Language Model with Self Generated Instructions for more details.
Example
from easyinstruct import SelfInstructGenerator
from easyinstruct.utils.api import set_openai_key
# Step1: Set your own API-KEY
set_openai_key("YOUR-KEY")
# Step2: Declare a generator class
generator = SelfInstructGenerator(num_instructions_to_generate=10)
# Step3: Generate self-instruct data
generator.generate()
BacktranslationGenerator
is the class for the instruction generation method of Instruction Backtranslation. See Self-Alignment with Instruction Backtranslation for more details.
Example
from easyinstruct import BacktranslationGenerator
from easyinstruct.utils.api import set_openai_key
# Step1: Set your own API-KEY
set_openai_key("YOUR-KEY")
# Step2: Declare a generator class
generator = BacktranslationGenerator(num_instructions_to_generate=10)
# Step3: Generate backtranslation data
generator.generate()
EvolInstructGenerator
is the class for the instruction generation method of EvolInstruct. See WizardLM: Empowering Large Language Models to Follow Complex Instructions for more details.
Example
from easyinstruct import EvolInstructGenerator
from easyinstruct.utils.api import set_openai_key
# Step1: Set your own API-KEY
set_openai_key("YOUR-KEY")
# Step2: Declare a generator class
generator = EvolInstructGenerator(num_instructions_to_generate=10)
# Step3: Generate evolution data
generator.generate()
KG2InstructGenerator
is the class for the instruction generation method of KG2Instruct. See InstructIE: A Chinese Instruction-based Information Extraction Dataset for more details.
The Selectors
module standardizes the instruction selection process, enabling the extraction of high-quality instruction datasets from raw, unprocessed instruction data. The raw data can be sourced from publicly available instruction datasets or generated by the framework itself. You can choose the appropriate selector based on your specific needs.
BaseSelector
is the base class for all selectors.
You can also easily inherit this base class to customize your own selector class. Just override the
__init__
and__process__
method.
Deduplicator
is the class for eliminating duplicate instruction samples that could adversely affect both pre-training stability and the performance of LLMs.Deduplicator
can also enables efficient use and optimization of storage space.
LengthSelector
is the class for selecting instruction samples based on the length of the instruction. Instructions that are too long or too short can affect data quality and are not conducive to instruction tuning.
RougeSelector
is the class for selecting instruction samples based on the ROUGE metric which is often used for evaluating the quality of automated generation of text.
GPTScoreSelector
is the class for selecting instruction samples based on the GPT score, which reflects whether the output is a good example of how AI Assistant should respond to the user's instruction, provided by ChatGPT.
PPLSelector
is the class for selecting instruction samples based on the perplexity, which is the exponentiated average negative log-likelihood of response.
MTLDSelector
is the class for selecting instruction samples based on the MTLD, which is short for Measure of Textual Lexical Diversity.
CodeSelector
is the class for selecting code instruction samples based on the Complexity-Impacted Reasoning Score (CIRS), which combines structural and logical attributes, to measure the correlation between code and reasoning abilities. See When Do Program-of-Thoughts Work for Reasoning? for more details.
Example
from easyinstruct import CodeSelector
# Step1: Specify your source file of code instructions
src_file = "data/code_example.json"
# Step2: Declare a code selecter class
selector = CodeSelector(
source_file_path=src_file,
target_dir="data/selections/",
manually_partion_data=True,
min_boundary = 0.125,
max_boundary = 0.5,
automatically_partion_data = True,
k_means_cluster_number = 2,
)
# Step3: Process the code instructions
selector.process()
MultiSelector
is the class for combining multiple appropricate selectors based on your specific needs.
The Prompts
module standardizes the instruction prompting step, where user requests are constructed as instruction prompts and sent to specific LLMs to obtain responses. You can choose the appropriate prompting method based on your specific needs.
Please check out link for more detials.
The Engines
module standardizes the instruction execution process, enabling the execution of instruction prompts on specific locally deployed LLMs. You can choose the appropriate engine based on your specific needs.
Please check out link for more detials.
Please cite our repository if you use EasyInstruct in your work.
@article{ou2024easyinstruct,
title={EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models},
author={Ou, Yixin and Zhang, Ningyu and Gui, Honghao and Xu, Ziwen and Qiao, Shuofei and Bi, Zhen and Chen, Huajun},
journal={arXiv preprint arXiv:2402.03049},
year={2024}
}
@misc{knowlm,
author = {Ningyu Zhang and Jintian Zhang and Xiaohan Wang and Honghao Gui and Kangwei Liu and Yinuo Jiang and Xiang Chen and Shengyu Mao and Shuofei Qiao and Yuqi Zhu and Zhen Bi and Jing Chen and Xiaozhuan Liang and Yixin Ou and Runnan Fang and Zekun Xi and Xin Xu and Lei Li and Peng Wang and Mengru Wang and Yunzhi Yao and Bozhong Tian and Yin Fang and Guozhou Zheng and Huajun Chen},
title = {KnowLM: An Open-sourced Knowledgeable Large Langugae Model Framework},
year = {2023},
url = {http://knowlm.zjukg.cn/},
}
@article{bi2023program,
title={When do program-of-thoughts work for reasoning?},
author={Bi, Zhen and Zhang, Ningyu and Jiang, Yinuo and Deng, Shumin and Zheng, Guozhou and Chen, Huajun},
journal={arXiv preprint arXiv:2308.15452},
year={2023}
}
We will offer long-term maintenance to fix bugs, solve issues and meet new requests. So if you have any problems, please put issues to us.
Other Related Projects
🙌 We would like to express our heartfelt gratitude for the contribution of Self-Instruct to our project, as we have utilized portions of their source code in our project.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for EasyInstruct
Similar Open Source Tools

EasyInstruct
EasyInstruct is a Python package proposed as an easy-to-use instruction processing framework for Large Language Models (LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.

atropos
Atropos is a robust and scalable framework for Reinforcement Learning Environments with Large Language Models (LLMs). It provides a flexible platform to accelerate LLM-based RL research across diverse interactive settings. Atropos supports multi-turn and asynchronous RL interactions, integrates with various inference APIs, offers a standardized training interface for experimenting with different RL algorithms, and allows for easy scalability by launching more environment instances. The framework manages diverse environment types concurrently for heterogeneous, multi-modal training.

LLM-Pruner
LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

basiclingua-LLM-Based-NLP
BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

DevDocs
DevDocs is a platform designed to simplify the process of digesting technical documentation for software engineers and developers. It automates the extraction and conversion of web content into markdown format, making it easier for users to access and understand the information. By crawling through child pages of a given URL, DevDocs provides a streamlined approach to gathering relevant data and integrating it into various tools for software development. The tool aims to save time and effort by eliminating the need for manual research and content extraction, ultimately enhancing productivity and efficiency in the development process.

RainbowGPT
RainbowGPT is a versatile tool that offers a range of functionalities, including Stock Analysis for financial decision-making, MySQL Management for database navigation, and integration of AI technologies like GPT-4 and ChatGlm3. It provides a user-friendly interface suitable for all skill levels, ensuring seamless information flow and continuous expansion of emerging technologies. The tool enhances adaptability, creativity, and insight, making it a valuable asset for various projects and tasks.

MetaGPT
MetaGPT is a multi-agent framework that enables GPT to work in a software company, collaborating to tackle more complex tasks. It assigns different roles to GPTs to form a collaborative entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, documents, etc. Internally, MetaGPT includes product managers, architects, project managers, and engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. MetaGPT's core philosophy is "Code = SOP(Team)", materializing SOP and applying it to teams composed of LLMs.

sec-parser
The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

langmanus
LangManus is a community-driven AI automation framework that combines language models with specialized tools for tasks like web search, crawling, and Python code execution. It implements a hierarchical multi-agent system with agents like Coordinator, Planner, Supervisor, Researcher, Coder, Browser, and Reporter. The framework supports LLM integration, search and retrieval tools, Python integration, workflow management, and visualization. LangManus aims to give back to the open-source community and welcomes contributions in various forms.

TokenFormer
TokenFormer is a fully attention-based neural network architecture that leverages tokenized model parameters to enhance architectural flexibility. It aims to maximize the flexibility of neural networks by unifying token-token and token-parameter interactions through the attention mechanism. The architecture allows for incremental model scaling and has shown promising results in language modeling and visual modeling tasks. The codebase is clean, concise, easily readable, state-of-the-art, and relies on minimal dependencies.

Neurite
Neurite is an innovative project that combines chaos theory and graph theory to create a digital interface that explores hidden patterns and connections for creative thinking. It offers a unique workspace blending fractals with mind mapping techniques, allowing users to navigate the Mandelbrot set in real-time. Nodes in Neurite represent various content types like text, images, videos, code, and AI agents, enabling users to create personalized microcosms of thoughts and inspirations. The tool supports synchronized knowledge management through bi-directional synchronization between mind-mapping and text-based hyperlinking. Neurite also features FractalGPT for modular conversation with AI, local AI capabilities for multi-agent chat networks, and a Neural API for executing code and sequencing animations. The project is actively developed with plans for deeper fractal zoom, advanced control over node placement, and experimental features.

UFO
UFO is a UI-focused dual-agent framework to fulfill user requests on Windows OS by seamlessly navigating and operating within individual or spanning multiple applications.

open-unlearning
OpenUnlearning is an easily extensible framework that unifies LLM unlearning evaluation benchmarks. It provides efficient implementations of TOFU and MUSE unlearning benchmarks, supporting 5 unlearning methods, 3+ datasets, 6+ evaluation metrics, and 7+ LLMs. Users can easily extend the framework to incorporate more variants, collaborate by adding new benchmarks, unlearning methods, datasets, and evaluation metrics, and drive progress in the field.

graphiti
Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

LLMBox
LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

KlicStudio
Klic Studio is a versatile audio and video localization and enhancement solution developed by Krillin AI. This minimalist yet powerful tool integrates video translation, dubbing, and voice cloning, supporting both landscape and portrait formats. With an end-to-end workflow, users can transform raw materials into beautifully ready-to-use cross-platform content with just a few clicks. The tool offers features like video acquisition, accurate speech recognition, intelligent segmentation, terminology replacement, professional translation, voice cloning, video composition, and cross-platform support. It also supports various speech recognition services, large language models, and TTS text-to-speech services. Users can easily deploy the tool using Docker and configure it for different tasks like subtitle translation, large model translation, and optional voice services.
For similar tasks

EasyInstruct
EasyInstruct is a Python package proposed as an easy-to-use instruction processing framework for Large Language Models (LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.

forevervm
foreverVM is a tool that provides an API for running arbitrary, stateful Python code securely. It revolves around the concepts of machines and instructions, where machines represent stateful Python processes and instructions are Python statements and expressions that can be executed on these machines. Users can interact with machines, run instructions, and receive results. The tool ensures that machines are managed efficiently by automatically swapping them from memory to disk when idle and back when needed, allowing for running REPLs 'forever'. Users can easily get started with foreverVM using the CLI and an API token, and can leverage the SDK for more advanced functionalities.
For similar jobs

EasyInstruct
EasyInstruct is a Python package proposed as an easy-to-use instruction processing framework for Large Language Models (LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.