Instructions to use adrianjoheni/model_data10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adrianjoheni/model_data10 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="adrianjoheni/model_data10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("adrianjoheni/model_data10") model = AutoModelForSeq2SeqLM.from_pretrained("adrianjoheni/model_data10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from adrianjoheni/model_data10: direct link, hf CLI and curl.
- Browser
- Download file 3.7 kB
-
https://huggingface.co/adrianjoheni/model_data10/resolve/main/training_args.bin
- Command line
-
hf download hf://adrianjoheni/model_data10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adrianjoheni/model_data10/resolve/main/training_args.bin
3.7 kB
- Xet hash:
- 9f3110fa314a0abbb325886ff040275ef4fc64781aefeab346b17a1bbdb43c89
- Size of remote file:
- 3.7 kB
- SHA256:
- fcd275c039ca2eda439bfc19a272d84ea14901721823d96352f9c30342d90eae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.