Instructions to use SEBIS/code_trans_t5_small_transfer_learning_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_transfer_learning_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SEBIS/code_trans_t5_small_transfer_learning_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SEBIS/code_trans_t5_small_transfer_learning_pretrain: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/SEBIS/code_trans_t5_small_transfer_learning_pretrain/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://SEBIS/code_trans_t5_small_transfer_learning_pretrain/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SEBIS/code_trans_t5_small_transfer_learning_pretrain/resolve/main/flax_model.msgpack
242 MB
- Xet hash:
- c38bad61659437e99ffe74faa3c976caf847ab0dacd4419143e53bbacd35e7ed
- Size of remote file:
- 242 MB
- SHA256:
- adcf32e01c0db291a80bca21e8074850cc4cbedc67102679f9fa437377b60755
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