WebGLM

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 1,600)

WebGLM: An Efficient Web-enhanced Question Answering System (KDD 2023)

WebGLM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<h1>WebGLM: Towards An Efficient Web-enhanced Question Answering System with Human Preferences</h1>

📃 <a href="https://arxiv.org/pdf/2306.07906.pdf" target="_blank">Paper (KDD'23)</a> • 🌐 <a href="https://github.com/THUDM/WebGLM/blob/main/README_zh.md" target="_blank">中文 README</a> • 🤗 HF Repo <a href="https://huggingface.co/THUDM/WebGLM" target="_blank">[WebGLM-10B]</a> <a href="https://huggingface.co/THUDM/WebGLM-2B" target="_blank">[WebGLM-2B]</a> • 📚 Dataset <a href="https://huggingface.co/datasets/THUDM/webglm-qa" target="_blank">[WebGLM-QA]</a>

This is the official implementation of WebGLM. If you find our open-sourced efforts useful, please 🌟 the repo to encourage our following developement!

**[Please click to watch the demo!]**

[![Click to Watch Demo!](https://img.youtube.com/vi/ohjrlYCLLEU/0.jpg)](https://www.youtube.com/watch?v=ohjrlYCLLEU)

_Read this in [中文](README_zh.md)._

## Update
**[2023/06/25]** Release [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B), an updated version of [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) which introduces several new features:

1. **Stronger Performance**: we have fully upgraded the ChatGLM2-6B. It uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official  implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.

More details please refer to [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B)。

-   [Overview](#overview)
    -   [Features](#features)
-   [News](#news)
-   [Preparation](#preparation)
    -   [Prepare Code and Environments](#prepare-code-and-environments)
    -   [Prepare SerpAPI Key](#prepare-serpapi-key)
    -   [Prepare Retriever Checkpoint](#prepare-retriever-checkpoint)
-   [Try WebGLM](#try-webglm)
    -   [Export Environment Variables](#export-environment-variables)
    -   [Run as Command Line Interface](#run-as-command-line-interface)
    -   [Run as Web Service](#run-as-web-service)
-   [Train WebGLM](#train-webglm)
    -   [Train Generator](#train-generator)
        -   [Prepare Data](#prepare-data)
        -   [Training](#training)
    -   [Train Retriever](#train-retriever)
        -   [Prepare Data](#prepare-data-1)
        -   [Training](#training-1)
-   [Evaluation](#evaluation)
-   [Real Application Cases](#real-application-cases)
-   [Citation](#citation)

# Overview

![paper](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/main_process.png)

WebGLM aspires to provide an efficient and cost-effective web-enhanced question-answering system using the 10-billion-parameter General Language Model (GLM). It aims to improve real-world application deployment by integrating web search and retrieval capabilities into the pre-trained language model.

## Features

-   **LLM-augmented Retriever**: Enhances the retrieval of relevant web content to better aid in answering questions accurately.
-   **Bootstrapped Generator**: Generates human-like responses to questions, leveraging the power of the GLM to provide refined answers.
-   **Human Preference-aware Scorer**: Estimates the quality of generated responses by prioritizing human preferences, ensuring the system produces useful and engaging content.

# News

-   **[2023-06-24]** We support searching via [Bing](https://www.bing.com/) now!
-   **[2023-06-14]** We release our code and the [paper](https://arxiv.org/pdf/2306.07906.pdf) of WebGLM!

# Preparation

## Prepare Code and Environments

Clone this repo, and install python requirements.

```bash
pip install -r requirements.txt
```

Install Nodejs.

```bash
apt install nodejs # If you use Ubuntu
```

Install playwright dependencies.

```bash
playwright install
```

If browsing environments are not installed in your host, you need to install them. Do not worry, playwright will give you instructions when you first execute it if so.

## Prepare SerpAPI Key

In search process, we use SerpAPI to get search results. You need to get a SerpAPI key from [here](https://serpapi.com/).

Then, set the environment variable `SERPAPI_KEY` to your key.

```bash
export SERPAPI_KEY="YOUR KEY"
```

Alternatively, you can use Bing search with local browser environment (playwright). You can add `--searcher bing` to start command lines to use Bing search. (See [Run as Command Line Interface](#run-as-command-line-interface) and [Run as Web Service](#run-as-web-service))

## Prepare Retriever Checkpoint

Download the checkpoint on [ModelScope](https://www.modelscope.cn/models/shawliu9/webglm-contriever) by running the command line below.

You can manually specify the path to save the checkpoint by `--save SAVE_PATH`.

```bash
python download.py retriever-pretrained-checkpoint
```

# Try WebGLM

Before you run the code, make sure that the space of your device is enough.

## Export Environment Variables

Export the environment variable `WEBGLM_RETRIEVER_CKPT` to the path of the retriever checkpoint. If you have downloaded the retriever checkpoint in the default path, you can simply run the command line below.

```bash
export WEBGLM_RETRIEVER_CKPT=./download/retriever-pretrained-checkpoint
```

## Run as Command Line Interface

You can try WebGLM-2B model by:

```bash
python cli_demo.py -w THUDM/WebGLM-2B
```

Or directly for WebGLM-10B model:

```bash
python cli_demo.py
```

If you want to use Bing search instead of SerpAPI, you can add `--searcher bing` to the command line, for example:

```bash
python cli_demo.py -w THUDM/WebGLM-2B --searcher bing
```

## Run as Web Service

Run `web_demo.py` with the same arguments as `cli_demo.py` to start a web service.
For example, you can try WebGLM-2B model with Bing search by:

```bash
python web_demo.py -w THUDM/WebGLM-2B --searcher bing
```

# Train WebGLM

## Train Generator

### Prepare Data (WebGLM-QA)

Download the training data (WebGLM-QA) on [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/d290dcfc92e342f9a017/) by running the command line below.

```bash
python download.py generator-training-data
```

It will automatically download all the data and preprocess them into the seq2seq form that can be used immediately in `./download`.

### Training

Please refer to [GLM repo](https://github.com/THUDM/GLM#train-with-your-own-data) for seq2seq training.

## Train Retriever

### Prepare Data

Download the training data on [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/3927b67a834c475288e2/) by running the command line below.

```bash
python download.py retriever-training-data
```

### Training

Run the following command line to train the retriever. If you have downloaded the retriever training data in the default path, you can simply run the command line below.

```bash
python train_retriever.py --train_data_dir ./download/retriever-training-data
```

# Evaluation

You can reproduce our results on TriviaQA, WebQuestions and NQ Open. Take TriviaQA for example, you can simply run the command line below:

```bash
bash scripts/triviaqa.sh
```

and start running the experiment.

# Real Application Cases

[Here](assets/cases) you can see some examples of WebGLM real application scenarios.

<details><summary>When will the COVID-19 disappear?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/0.png)

</details>

<details><summary>How to balance career and hobbies?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/1.png)

</details>

<details><summary>FL Studio and Cubase, which is better?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/2.png)

</details>

<details><summary>Is attention better than CNN?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/3.png)

</details>

<details><summary>How to survive in the first-tier cities without a high-salary work?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/4.png)

</details>

<details><summary>What do you think of version 3.5 of Genshin Impact?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/5.png)

</details>

<details><summary>transformers are originated in NLP, but why they can be applied in CV?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/6.png)

</details>

<details><summary>Who proposed Music Transformer? How does it work?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/7.png)

</details>

<details><summary>What is the backbone of Toolformer?</summary>

![](https://raw.githubusercontent.com/THUDM/WebGLM/main/assets/cases/8.png)

</details>

# License

This repository is licensed under the [Apache-2.0 License](LICENSE). The use of model weights is subject to the [Model_License](MODEL_LICENSE). All open-sourced data is for resarch purpose only.

# Citation

If you use this code for your research, please cite our paper.

```
@misc{liu2023webglm,
      title={WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences},
      author={Xiao Liu and Hanyu Lai and Hao Yu and Yifan Xu and Aohan Zeng and Zhengxiao Du and Peng Zhang and Yuxiao Dong and Jie Tang},
      year={2023},
      eprint={2306.07906},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

> This repo is simplified for easier deployment.

WebGLM

WebGLM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

WebGLM

WebGLM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README