Instructions to use michaelnath/dummy_code_to_code_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelnath/dummy_code_to_code_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("michaelnath/dummy_code_to_code_model") model = AutoModelForSeq2SeqLM.from_pretrained("michaelnath/dummy_code_to_code_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from michaelnath/dummy_code_to_code_model: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/michaelnath/dummy_code_to_code_model/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://michaelnath/dummy_code_to_code_model@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/michaelnath/dummy_code_to_code_model/resolve/refs%2Fpr%2F1/model.safetensors
892 MB
- Xet hash:
- cccfafd367fffc8a390db46855026cebaf5c9ca2635c3c918b15cac0ba014bf5
- Size of remote file:
- 892 MB
- SHA256:
- 527e3c85a7871867111b9ed4edba74056db74132159136004ba6c159c241b2aa
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