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

Related tags

Text Data & NLPapitext-generationgptlanguage-modelfastapigpt-3gpt-j
Overview

gpt-j-api 🦜

GitHub release (latest by date) Python version API up

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

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

    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

    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

    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

    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?

    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?

    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

    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?

    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?

    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...

    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

    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

    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)
Owner
Víctor Gallego
Data scientist & predoc researcher
Víctor Gallego
GitHub Repository http://api.vicgalle.net:8000/
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
End-to-end text to speech system using gruut and onnx. There are 40 voices available across 8 languages.

End to end text to speech system using gruut and onnx

Rhasspy 673 Dec 28, 2022
Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

9 Jan 08, 2023
This is the offline-training-pipeline for our project.

offline-training-pipeline This is the offline-training-pipeline for our project. We adopt the offline training and online prediction Machine Learning

0 Apr 22, 2022
A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

Basic-UI-for-GPT-J-6B-with-low-vram A repository to run GPT-J-6B on low vram systems by using both ram, vram and pinned memory. There seem to be some

90 Dec 25, 2022
An IVR Chatbot which can exponentially reduce the burden of companies as well as can improve the consumer/end user experience.

IVR-Chatbot Achievements 🏆 Team Uhtred won the Maverick 2.0 Bot-a-thon 2021 organized by AbInbev India. ❓ Problem Statement As we all know that, lot

ARYAMAAN PANDEY 9 Dec 08, 2022
Paradigm Shift in NLP - "Paradigm Shift in Natural Language Processing".

Paradigm Shift in NLP Welcome to the webpage for "Paradigm Shift in Natural Language Processing". Some resources of the paper are constantly maintaine

Tianxiang Sun 41 Dec 30, 2022
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.

Transformer Embedder A Word Level Transformer layer based on PyTorch and 🤗 Transformers. How to use Install the library from PyPI: pip install transf

Riccardo Orlando 27 Nov 20, 2022
Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

Introduction The goal of this analysis is to find a model that fits the observed cumulative cases of COVID-19 in the US, starting in Mid-July 2021 and

Alexander Keeney 1 Jan 05, 2022
Open-source offline translation library written in Python. Uses OpenNMT for translations

Open source neural machine translation in Python. Designed to be used either as a Python library or desktop application. Uses OpenNMT for translations and PyQt for GUI.

Argos Open Tech 1.6k Jan 01, 2023
Ask for weather information like a human

weather-nlp About Ask for weather information like a human. Goals Understand typical questions like: Hourly temperatures in Potsdam on 2020-09-15. Rai

5 Oct 29, 2022
Gpt2-WebAPI - The objective of this API is to provide the 3 best possible responses to sentences that the user would input via http GET request as a parameter

This repository is a modification of: https://github.com/openai/gpt-2 for our sp

Paulina 1 Jan 06, 2022
A curated list of efficient attention modules

awesome-fast-attention A curated list of efficient attention modules

Sepehr Sameni 891 Dec 22, 2022
This is a NLP based project to extract effective date of the contract from their text files.

Date-Extraction-from-Contracts This is a NLP based project to extract effective date of the contract from their text files. Problem statement This is

Sambhav Garg 1 Jan 26, 2022
New Modeling The Background CodeBase

Modeling the Background for Incremental Learning in Semantic Segmentation This is the updated official PyTorch implementation of our work: "Modeling t

Fabio Cermelli 9 Dec 28, 2022
Label data using HuggingFace's transformers and automatically get a prediction service

Label Studio for Hugging Face's Transformers Website • Docs • Twitter • Join Slack Community Transfer learning for NLP models by annotating your textu

Heartex 135 Dec 29, 2022
用Resnet101+GPT搭建一个玩王者荣耀的AI

基于pytorch框架用resnet101加GPT搭建AI玩王者荣耀 本源码模型主要用了SamLynnEvans Transformer 的源码的解码部分。以及pytorch自带的预训练模型"resnet101-5d3b4d8f.pth"

冯泉荔 2.2k Jan 03, 2023
A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.

MedMCQA MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering A large-scale, Multiple-Choice Question Answe

MedMCQA 24 Nov 30, 2022
MILES is a multilingual text simplifier inspired by LSBert - A BERT-based lexical simplification approach proposed in 2018. Unlike LSBert, MILES uses the bert-base-multilingual-uncased model, as well as simple language-agnostic approaches to complex word identification (CWI) and candidate ranking.

MILES Multilingual Lexical Simplifier Explore the docs » Read LSBert Paper · Report Bug · Request Feature About The Project MILES is a multilingual te

Kane 45 Oct 19, 2022
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

LEE YOON HYUNG 147 Dec 05, 2022