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

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
Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA

Named Entity Recognition API with spaCy and GiNZA I wrote a blog post about this

Yuki Okuda 3 Feb 27, 2022
Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers and helping them make a wise buying decision.

Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers an

Parv Bhatt 1 Jan 01, 2022
PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI

data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F

Aryan Shekarlaban 105 Jan 04, 2023
Legal text retrieval for python

legal-text-retrieval Overview This system contains 2 steps: generate training data containing negative sample found by mixture score of cosine(tfidf)

Nguyễn Minh Phương 22 Dec 06, 2022
End-to-end MLOps pipeline of a BERT model for emotion classification.

image source EmoBERT-MLOps The goal of this repository is to build an end-to-end MLOps pipeline based on the MLOps course from Made with ML, but this

Dimitre Oliveira 4 Nov 06, 2022
Library for Russian imprecise rhymes generation

TOM RHYMER Library for Russian imprecise rhymes generation. Quick Start Generate rhymes by any given rhyme scheme (aabb, abab, aaccbb, etc ...): from

Alexey Karnachev 6 Oct 18, 2022
A python package to fine-tune transformer-based models for named entity recognition (NER).

nerblackbox A python package to fine-tune transformer-based language models for named entity recognition (NER). Resources Source Code: https://github.

Felix Stollenwerk 13 Jul 30, 2022
Using Bert as the backbone model for lime, designed for NLP task explanation (sentence pair text classification task)

Lime Comparing deep contextualized model for sentences highlighting task. In addition, take the classic explanation model "LIME" with bert-base model

JHJu 2 Jan 18, 2022
I can help you convert your images to pdf file.

IMAGE TO PDF CONVERTER BOT Configs TOKEN - Get bot token from @BotFather API_ID - From my.telegram.org API_HASH - From my.telegram.org Deploy to Herok

MADUSHANKA 10 Dec 14, 2022
:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

Dedupe Python Library dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on

Dedupe.io 3.6k Jan 02, 2023
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng

Mark Dong 166 Dec 11, 2022
Simple Text-To-Speech Bot For Discord

Simple Text-To-Speech Bot For Discord This is a very simple TTS bot for discord made with python. For this bot you need FFMPEG, see installation to se

1 Sep 26, 2022
A natural language modeling framework based on PyTorch

Overview PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapi

Meta Research 6.4k Jan 08, 2023
Quick insights from Zoom meeting transcripts using Graph + NLP

Transcript Analysis - Graph + NLP This program extracts insights from Zoom Meeting Transcripts (.vtt) using TigerGraph and NLTK. In order to run this

Advit Deepak 7 Sep 17, 2022
Beautiful visualizations of how language differs among document types.

Scattertext 0.1.0.0 A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot. Points corresponding t

Jason S. Kessler 2k Dec 27, 2022
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au

pyannote 2.2k Jan 09, 2023
Nmt - TensorFlow Neural Machine Translation Tutorial

Neural Machine Translation (seq2seq) Tutorial Authors: Thang Luong, Eugene Brevdo, Rui Zhao (Google Research Blogpost, Github) This version of the tut

6.1k Dec 29, 2022
Easy to start. Use deep nerual network to predict the sentiment of movie review.

Easy to start. Use deep nerual network to predict the sentiment of movie review. Various methods, word2vec, tf-idf and df to generate text vectors. Various models including lstm and cov1d. Achieve f1

1 Nov 19, 2021
Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

Espresso Espresso is an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning libra

Yiming Wang 919 Jan 03, 2023
BiNE: Bipartite Network Embedding

BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiang

leihuichen 214 Nov 24, 2022