Instructions to use BigTMiami/tapt_helpfulness_base_pretraining_model_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/tapt_helpfulness_base_pretraining_model_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BigTMiami/tapt_helpfulness_base_pretraining_model_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BigTMiami/tapt_helpfulness_base_pretraining_model_final") model = AutoModelForMaskedLM.from_pretrained("BigTMiami/tapt_helpfulness_base_pretraining_model_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigTMiami/tapt_helpfulness_base_pretraining_model_final: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/BigTMiami/tapt_helpfulness_base_pretraining_model_final/resolve/main/training_args.bin
- Command line
-
hf download hf://BigTMiami/tapt_helpfulness_base_pretraining_model_final/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigTMiami/tapt_helpfulness_base_pretraining_model_final/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 0a2c896953fb5e553d96891d3ce2557a4fac018e15a9fc1e6531252192446561
- Size of remote file:
- 4.98 kB
- SHA256:
- 629fd033b0bbbe55caa79e38428a258835d4043942f4f20b2d69f3a47bd58c9d
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