Instructions to use SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune/resolve/refs%2Fpr%2F3/flax_model.msgpack
- Command line
-
hf download hf://SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune@refs/pr/3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune/resolve/refs%2Fpr%2F3/flax_model.msgpack
242 MB
- Xet hash:
- fc417f7b3ed3b2fdcecb14b2739f975f7d97a3f5fb040ed7170627ffb545c092
- Size of remote file:
- 242 MB
- SHA256:
- 2ec5eaa99d63c40c50cc715764603f61f27081c2074811012dc86a92c173f2e4
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