Instructions to use disanda/first_try_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use disanda/first_try_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="disanda/first_try_4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("disanda/first_try_4") model = AutoModelForMaskedLM.from_pretrained("disanda/first_try_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from disanda/first_try_4: direct link, hf CLI and curl.
- Browser
- Download file 3.89 kB
-
https://huggingface.co/disanda/first_try_4/resolve/main/training_args.bin
- Command line
-
hf download hf://disanda/first_try_4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/disanda/first_try_4/resolve/main/training_args.bin
3.89 kB
- Xet hash:
- 0bf72fc447fb9a3c161806c549a0aec59ae5cdbad75a175667516ccc866d3e4c
- Size of remote file:
- 3.89 kB
- SHA256:
- f5af8bc733e5b754b9cb1c745567b23aa32a7c4507bbdf458433a4a38b91a77c
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