Instructions to use BigTMiami/micro_base_help_tapt_pretrain_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigTMiami/micro_base_help_tapt_pretrain_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BigTMiami/micro_base_help_tapt_pretrain_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BigTMiami/micro_base_help_tapt_pretrain_model") model = AutoModelForMaskedLM.from_pretrained("BigTMiami/micro_base_help_tapt_pretrain_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigTMiami/micro_base_help_tapt_pretrain_model: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/BigTMiami/micro_base_help_tapt_pretrain_model/resolve/main/training_args.bin
- Command line
-
hf download hf://BigTMiami/micro_base_help_tapt_pretrain_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigTMiami/micro_base_help_tapt_pretrain_model/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 81a57445fee0f28e81a97b66b61e0a4e6ace09d457b8b619a7326edc7d5c6f1c
- Size of remote file:
- 4.73 kB
- SHA256:
- cbf33847918e192c32e72b8b30d2e698e5d50dee4535c23a4568c87105b6b29d
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