Instructions to use SushantGautam/CodeGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SushantGautam/CodeGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SushantGautam/CodeGeneration")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SushantGautam/CodeGeneration") model = AutoModelForMaskedLM.from_pretrained("SushantGautam/CodeGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from SushantGautam/CodeGeneration: direct link, hf CLI and curl.
- Browser
- Download file 324 Bytes
-
https://huggingface.co/SushantGautam/CodeGeneration/resolve/main/predict_results.json
- Command line
-
hf download hf://SushantGautam/CodeGeneration/predict_results.json
-
curl -L -o predict_results.json https://huggingface.co/SushantGautam/CodeGeneration/resolve/main/predict_results.json
324 Bytes
| { | |
| "predict_gen_len": 1.0, | |
| "predict_loss": 5.266044616699219, | |
| "predict_rouge1": 0.0, | |
| "predict_rouge2": 0.0, | |
| "predict_rougeL": 0.0, | |
| "predict_rougeLsum": 0.0, | |
| "predict_runtime": 14.8282, | |
| "predict_samples": 3527, | |
| "predict_samples_per_second": 237.858, | |
| "predict_steps_per_second": 14.904 | |
| } |