Feature Extraction
Transformers
PyTorch
roberta
code-understanding
unixcoder
text-embeddings-inference
Instructions to use Henry65/RepoSim4Py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Henry65/RepoSim4Py with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Henry65/RepoSim4Py")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Henry65/RepoSim4Py") model = AutoModel.from_pretrained("Henry65/RepoSim4Py", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Henry65/RepoSim4Py: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/Henry65/RepoSim4Py/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Henry65/RepoSim4Py/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Henry65/RepoSim4Py/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- c6abd1868f23c2323ca651fe3157b7934d0918e34e397963e3c476f072302a6d
- Size of remote file:
- 504 MB
- SHA256:
- ec359ccef197b85f9cea791cc2af5728aafd1adcd06713e2a3fb8290c43df3e3
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