Instructions to use SEBIS/code_trans_t5_base_api_generation_multitask_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_api_generation_multitask_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_base_api_generation_multitask_finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SEBIS/code_trans_t5_base_api_generation_multitask_finetune: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/SEBIS/code_trans_t5_base_api_generation_multitask_finetune/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SEBIS/code_trans_t5_base_api_generation_multitask_finetune/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SEBIS/code_trans_t5_base_api_generation_multitask_finetune/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- 838339e3a0cfe9720e2cdb159d5ab75e055bbec7eb8a7c1216d08620c2e889af
- Size of remote file:
- 892 MB
- SHA256:
- 676d0f6485350c74244a6fd541661e5d2c2d0f18bb45e05aecd49215bad81e33
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