Instructions to use BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model") model = AutoModelForMaskedLM.from_pretrained("BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model/resolve/main/training_args.bin
- Command line
-
hf download hf://BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigTMiami/dapt_plus_tapt_helpfulness_base_pretraining_model/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- b7a26c5bfec0053fefe86fed89b0e5c3b093775ed0dd13a9f6296843a0cd2f7f
- Size of remote file:
- 4.98 kB
- SHA256:
- bef72ec9377f7ac0d50025b5da6c6a07b3fcc3020b79ccd022ceba020514e190
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