Instructions to use WesScivetti/GPT-BERT_Random_Seed2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/GPT-BERT_Random_Seed2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from WesScivetti/GPT-BERT_Random_Seed2: direct link, hf CLI and curl.
- Browser
- Download file 503 MB
-
https://huggingface.co/WesScivetti/GPT-BERT_Random_Seed2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://WesScivetti/GPT-BERT_Random_Seed2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/WesScivetti/GPT-BERT_Random_Seed2/resolve/main/pytorch_model.bin
503 MB
- Xet hash:
- 8f987a526ed22b07c7621cb4e55b159b8f004cf5389388557ad3dc2d3020a752
- Size of remote file:
- 503 MB
- SHA256:
- 94aed6fafd8e2772e02359880fdcc62b5c515eecc91606aa274a9e1d9f0e91a1
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