Instructions to use juliensimon/imdb-demo-infinity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliensimon/imdb-demo-infinity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juliensimon/imdb-demo-infinity")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("juliensimon/imdb-demo-infinity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/predict_infinity.py from juliensimon/imdb-demo-infinity: direct link, hf CLI and curl.
- Browser
- Download file 361 Bytes
-
https://huggingface.co/juliensimon/imdb-demo-infinity/resolve/main/code/predict_infinity.py
- Command line
-
hf download hf://juliensimon/imdb-demo-infinity/code/predict_infinity.py
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curl -L -o predict_infinity.py https://huggingface.co/juliensimon/imdb-demo-infinity/resolve/main/code/predict_infinity.py
361 Bytes
| import requests as r | |
| import time | |
| input_data = { "inputs": "Dune is something rarely seen anymore – it’s an epic."} | |
| url = "http://localhost:8080/predict" | |
| sum=0 | |
| for i in range(0,1000): | |
| response = r.post(url, json=input_data) | |
| resp_time = response.headers["x-compute-time"] | |
| sum+=float(resp_time) | |
| sum/=1000 | |
| print(f"{round(float(sum),6)*1000}ms") | |