Instructions to use sujalgawas/code-bert-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sujalgawas/code-bert-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sujalgawas/code-bert-python")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sujalgawas/code-bert-python") model = AutoModel.from_pretrained("sujalgawas/code-bert-python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download Qwen_model_v1.pt from sujalgawas/code-bert-python: direct link, hf CLI and curl.
- Browser
- Download file 507 MB
-
https://huggingface.co/sujalgawas/code-bert-python/resolve/682da54be45ebf80be0bb3aa69992a31bec29536/Qwen_model_v1.pt
- Command line
-
hf download hf://sujalgawas/code-bert-python@682da54be45ebf80be0bb3aa69992a31bec29536/Qwen_model_v1.pt
-
curl -L -o Qwen_model_v1.pt https://huggingface.co/sujalgawas/code-bert-python/resolve/682da54be45ebf80be0bb3aa69992a31bec29536/Qwen_model_v1.pt
507 MB
- Xet hash:
- 46556d1d5c0b9df307bbdcfde70a1823148e7705474ec2684c7431064df74a53
- Size of remote file:
- 507 MB
- SHA256:
- 5cb249562a55d9242542af7136d7a168bb50332eb91644502e195d1714c768f7
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