API for the GPT-J language model 🦜. Including a FastAPI backend and a streamlit frontend

Last update: Dec 31, 2022

Overview

gpt-j-api 🦜

An API to interact with the GPT-J language model. You can use and test the model in two different ways:

Streamlit web app at http://api.vicgalle.net:8000/
The proper API, documented at http://api.vicgalle.net:5000/docs

Using the API

Python:

import requests
context = "In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English."
payload = {
    "context": context,
    "token_max_length": 512,
    "temperature": 1.0,
    "top_p": 0.9,
}
response = requests.post("http://api.vicgalle.net:5000/generate", params=payload).json()
print(response)

Bash:

curl -X 'POST' \
  'http://api.vicgalle.net:5000/generate?context=In%20a%20shocking%20finding%2C%20scientists%20discovered%20a%20herd%20of%20unicorns%20living%20in%20a%20remote%2C%20previously%20unexplored%20valley%2C%20in%20the%20Andes%20Mountains.%20Even%20more%20surprising%20to%20the%20researchers%20was%20the%20fact%20that%20the%20unicorns%20spoke%20perfect%20English.&token_max_length=512&temperature=1&top_p=0.9' \
  -H 'accept: application/json' \
  -d ''

Deployment of the API server

Just ssh into a TPU VM. This code was only tested on the v3-8 variants.

First, install the requirements and get the weigts:

python3 -m pip install -r requirements.txt
wget https://the-eye.eu/public/AI/GPT-J-6B/step_383500_slim.tar.zstd
sudo apt install zstd
tar -I zstd -xf step_383500_slim.tar.zstd

And just run

python3 serve.py

Then, you can go to http://localhost:5000/docs and use the API!

Deploy the streamlit dashboard

Just run

python3 -m streamlit run streamlit_app.py --server.port 8000

Acknowledgements

Thanks to the support of the TPU Research Cloud, https://sites.research.google/trc/

Comments

I've made an extensions using this api

https://chrome.google.com/webstore/detail/type-j/femdhcgkiiagklmickakfoogeehbjnbh

You can check it out here

First i was very hyped up and it felt fun, like I was talking to a machine, but then I lost my enthusiasm and now I feel like it's totally useless xD

I'm just leaving a link here for you to appreciate you, it became real thanks for you posting this api

feel free to delete the issue as it's out of scope

if you got ideas on how to make it commercially succesful - i'll be happy to partner up

peace

opened by oogxdd 5

Illegal Instruction

When installing like described in the readme (fresh conda env,python=3.8, ubuntu) I'll get a illegal instruction immediately after running python serve.py

(gpt-j-api) […]@[…]:/opt/GPT/gpt-j-api$ python -q -X faulthandler serve.py
Fatal Python error: Illegal instruction

Current thread 0x00007f358d7861c0 (most recent call first):
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap_external>", line 1166 in create_module
  File "<frozen importlib._bootstrap>", line 556 in module_from_spec
  File "<frozen importlib._bootstrap>", line 657 in _load_unlocked
  File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 991 in _find_and_load
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1042 in _handle_fromlist
  File "/home/korny/miniconda3/envs/gpt-j-api/lib/python3.8/site-packages/jaxlib/xla_client.py", lin
e 31 in <module>
  File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 991 in _find_and_load
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1042 in _handle_fromlist
  File "/home/korny/miniconda3/envs/gpt-j-api/lib/python3.8/site-packages/jax/lib/__init__.py", line 58 in <module>
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap_external>", line 843 in exec_module
  File "<frozen importlib._bootstrap>", line 671 in _load_unlocked
  File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 991 in _find_and_load
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1042 in _handle_fromlist
  File "/home/korny/miniconda3/envs/gpt-j-api/lib/python3.8/site-packages/jax/config.py", line 26 in <module>
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap_external>", line 843 in exec_module
  File "<frozen importlib._bootstrap>", line 671 in _load_unlocked
  File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 991 in _find_and_load
  File "/home/korny/miniconda3/envs/gpt-j-api/lib/python3.8/site-packages/jax/__init__.py", line 33 in <module>
  File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
  File "<frozen importlib._bootstrap_external>", line 843 in exec_module
  File "<frozen importlib._bootstrap>", line 671 in _load_unlocked
  File "<frozen importlib._bootstrap>", line 975 in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 991 in _find_and_load
  File "serve.py", line 3 in <module>
Illegal instruction (core dumped)

EDIT

running this on CPU only, I tried installing jax[CPU] - same resut

opened by chris-aeviator 4

api seems offline

When I try to access the API I get the following error: ERR_CONNECTION_TIMED_OUT. But when I try to connect to it using a different IP address it does work. Am I IP banned?

opened by KoenTech 4
Usage

I'm using this to host a Discord chatbot, and though I have slowmode on the channel there's still a lot of usage, and often the API is being used as fast as it can generate completions. Will this harm the experience for others? Should I limit it more? (thanks for making this free but I don't want to take advantage of that too much if it's bad for others)

opened by Heath123 3
Alternative to Google TPU VM?

Hello,

I would like to run a local instance of GPT-J, but avoid using Google.

I have little to no experience in machine learning and its requirements, are there other solutions I could use? (What are the requirements for a machine in order to run GPT-J?)

Thank you very much!

opened by birkenbaum 3
Is there a way to speed up inference?

Hello, I am currently working on a project where I need quick inference. It needn't be real-time, but something around 7-10 sec would be great. Is there a way to speed up the inference using the API?

The model does not seem to be a problem as compute_time is around 8sec, but by the time the request arrives it takes around 20 seconds (over 30 on some occasions). Is there a way to make the request a bit faster?

Thanks,

opened by Aryagm 2
Errno 111

Could anyone please fix the following error? Thanks a lot.

"ConnectionError: ...Failed to establish a new connection: [Errno 111] Connection refused"

opened by Mather10 1
How to make the api public?

Hey, I was able to get serve.py running with the instructions you gave. But now I want to make the api public and connect it to a domain name so it can be publicly accessed (without needing a connection to the vm). How can I achieve this?

I want to do the same thing you did with "http://api.vicgalle.net:5000/generate" and "http://api.vicgalle.net:5000/docs".

Thanks,

opened by Aryagm 1
API VM?

Hi I wanted to host my own version of the api, where is the public one hosted? is it on a google cloud TPU VM? The ones ive seen here https://cloud.google.com/tpu/pricing are very expensive :D Is a TPU VM needed and the model won't be able to run on a normal GPU VM?

Thanks!

opened by jryebread 1
Raw text...

This is probably a very stupid question but whenever I run GPT-J I always get the full output:

{'model': 'GPT-J-6B', 'compute_time': 1.2492187023162842, 'text': ' \n(and you\'ll be a slave)\n\n**_"I\'m not a robot, I\'m a human being."_**\n\n**_"I\'m not a robot, I\'m a human being."_**\n\n', 'prompt': 'AI will take over the world ', 'token_max_length': 50, 'temperature': 0.09, 'top_p': 0.9, 'stop_sequence': None}

What parameter do I need to change so it only outputs the generated text?

(and you'll be a slave) I'm not a robot, I'm a human being. I'm not a robot, I'm a human being.

opened by Vilagamer999 1
Latency with TPU VM

Got things running on Google Clouds, really happy :). Was hoping for a little but of a speed increase, but computation time is the same and latency on the request seems to be the main delay. Did you experiment with firewalls and ports to improve things?

opened by Ontopic 1
Version support for Huggingface GPT-J 6B

GPT-J Huggingface and streamlit style like by project-code py

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")

model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")

opened by ghost 0

Releases(v0.3)

v0.3(Aug 8, 2021)
New feature:

New API endpoint: classify. Allows to use the model as zero-shot classification of texts. See the documentation

Source code(tar.gz)
Source code(zip)
v0.2.1(Jul 27, 2021)
New features:

Introduce a new argument stop_sequence in the API

Introduce tests, to ensure the API is live

Source code(tar.gz)
Source code(zip)
v0.2(Jul 8, 2021)

Now includes a streamlit web app that connects to the API
Source code(tar.gz)
Source code(zip)
v0.1(Jun 29, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Víctor Gallego

Data scientist & predoc researcher

GitHub Repository http://api.vicgalle.net:8000/

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Introduction This is a PyTorch implementation of the following research papers: (1) Hierarchical Text Generation and Planning for Strategic Dialogue (

1.4k Dec 29, 2022

Search msDS-AllowedToActOnBehalfOfOtherIdentity

前言现在进行RBCD的攻击手段主要是搜索mS-DS-CreatorSID，如果机器的创建者是我们可控的话，那就可以修改对应机器的msDS-AllowedToActOnBehalfOfOtherIdentity，利用工具SharpAllowedToAct-Modify 那我们索性也试试搜索所有计算机

26 Dec 05, 2022

ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python)

ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python) 日本語は以下に続きます (Japanese follows) English: This book is written in Japanese and primaril

189 Dec 29, 2022

The projects lets you extract glossary words and their definitions from a given piece of text automatically using NLP techniques

Unsupervised technique to Glossary and Definition Extraction Code Files GPT2-DefinitionModel.ipynb - GPT-2 model for definition generation. Data_Gener

28 May 25, 2021

This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

1.1k Dec 27, 2022

Python module (C extension and plain python) implementing Aho-Corasick algorithm

pyahocorasick pyahocorasick is a fast and memory efficient library for exact or approximate multi-pattern string search meaning that you can find mult

763 Dec 27, 2022

Harvis is designed to automate your C2 Infrastructure.

Harvis Harvis is designed to automate your C2 Infrastructure, currently using Mythic C2. 📌 What is it? Harvis is a python tool to help you create mul

99 Oct 06, 2022

Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

92 Dec 25, 2022

Easy, fast, effective, and automatic g-code compression!

Getting to the meat of g-code. Easy, fast, effective, and automatic g-code compression! MeatPack nearly doubles the effective data rate of a standard

97 Nov 21, 2022

Optimal Transport Tools (OTT), A toolbox for all things Wasserstein.

Optimal Transport Tools (OTT), A toolbox for all things Wasserstein. See full documentation for detailed info on the toolbox. The goal of OTT is to pr

255 Dec 26, 2022

Unsupervised text tokenizer for Neural Network-based text generation.

SentencePiece SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabu

6.4k Jan 01, 2023

Open Source Neural Machine Translation in PyTorch

OpenNMT-py: Open-Source Neural Machine Translation OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine trans

5.8k Jan 04, 2023

Outreachy TFX custom component project

Schema Curation Custom Component Outreachy TFX custom component project This repo contains the code for Schema Curation Custom Component made as a par

5 Jul 16, 2021

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP

TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack

2.2k Jan 03, 2023

Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET

Training COMET using seq2seq setting Use AutoModelForSeq2SeqLM in Huggingface Transformers to train COMET. The codes are modified from run_summarizati

9 Dec 17, 2022

Official PyTorch implementation of SegFormer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

1.4k Dec 29, 2022

💫 Industrial-strength Natural Language Processing (NLP) in Python

spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest researc

24.9k Jan 02, 2023

Mkdocs + material + cool stuff

Modern-Python-Doc-Example mkdocs + material + cool stuff Doc is live here Features out of the box amazing good looking website thanks to mkdocs.org an

61 Oct 26, 2022

A Japanese tokenizer based on recurrent neural networks

Nagisa is a python module for Japanese word segmentation/POS-tagging. It is designed to be a simple and easy-to-use tool. This tool has the following

325 Jan 05, 2023

🦆 Contextually-keyed word vectors

sense2vec: Contextually-keyed word vectors sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detaile

1.5k Dec 25, 2022