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API for the GPT-J language model 🦜. Including a FastAPI backend and a streamlit frontend
| Date | Stars |
|---|---|
| 2026-08-02 | 335 |
| 2026-08-25 | 336 |
| 2026-09-06 | 336 |
| 2026-09-21 | 335 |
| 2026-10-07 | 336 |
| 2026-11-04 | 336 |
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# gpt-j-api 🦜
[](https://huggingface.co/spaces/vicgalle/gpt-j-api)
An API to interact with the GPT-J language model and variants! 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
## Open API endpoints 🔓
These are the endpoints of the public API and require no authentication.
Click on each to see the parameters!
#### GPT-J text generation 🤖
* [generate](docs/generate.md) : `POST /generate/`
#### Zero-shot text classification (multilingual) 🌍
* [classify](docs/classify.md) : `POST /classify/`
## Using the API 🔥
* Python:
```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)
```
* Python (zero-shot classification):
```python
import requests
payload = {
"sequence" : "The movie started slow, but in the end was absolutely amazing!",
"labels" : "positive,neutral,negative"}
response = requests.post("http://api.vicgalle.net:5000/classify", params=payload).json()
print(response)
```
* Bash:
```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 tested on both the v2-8 and v3-8 variants.
First, install the requirements and get the weights:
```
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
rm 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
```
## Contact
If you have a request, I'll be happy to help you at `vgallegoalcala at gmail dot com`
## Sponsors 🦄
Special thanks to the following people, who sponsor this project! <3
* [Aspie96](https://github.com/Aspie96)
## Acknowledgements ✨
Many thanks to the support of the TPU Research Cloud, https://sites.research.google/trc/
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