Instructions to use michaelnath/glued_code_to_code_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelnath/glued_code_to_code_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("michaelnath/glued_code_to_code_model") model = AutoModelForSeq2SeqLM.from_pretrained("michaelnath/glued_code_to_code_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from michaelnath/glued_code_to_code_model: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/michaelnath/glued_code_to_code_model/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://michaelnath/glued_code_to_code_model@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/michaelnath/glued_code_to_code_model/resolve/refs%2Fpr%2F1/model.safetensors
892 MB
- Xet hash:
- bffa8ef09f0cdfdfe4c8d9cace2d968a520e6b6a61bb7cd8e4d646b939289509
- Size of remote file:
- 892 MB
- SHA256:
- dd22c7473118e913d7d7c0fefb6e6827158636c352311302d03269262b27bab3
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