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 spiece.model from SushantGautam/CodeGeneration: direct link, hf CLI and curl.
- Browser
- Download file 797 kB
-
https://huggingface.co/SushantGautam/CodeGeneration/resolve/main/spiece.model
- Command line
-
hf download hf://SushantGautam/CodeGeneration/spiece.model
-
curl -L -o spiece.model https://huggingface.co/SushantGautam/CodeGeneration/resolve/main/spiece.model
797 kB
- Xet hash:
- b2c7e8ef85ff0ceab80b31d0b61ba60ad52ffa9b842b89c4bb91bcd6e03722d9
- Size of remote file:
- 797 kB
- SHA256:
- 9856b76e9978cc5805f0566cedabd2fc7bdb1a3ee22d52545100c056cb09a59c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.