Instructions to use neuronstarml/samsuntextsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neuronstarml/samsuntextsum 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="neuronstarml/samsuntextsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("neuronstarml/samsuntextsum") model = AutoModelForSeq2SeqLM.from_pretrained("neuronstarml/samsuntextsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from neuronstarml/samsuntextsum: direct link, hf CLI and curl.
- Browser
- Download file 4.55 GB
-
https://huggingface.co/neuronstarml/samsuntextsum/resolve/main/optimizer.pt
- Command line
-
hf download hf://neuronstarml/samsuntextsum/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/neuronstarml/samsuntextsum/resolve/main/optimizer.pt
4.55 GB
- Xet hash:
- 79d4cf40a3f1d2d831cb4165b7658f45d5e736b96be49877f966976e51981f68
- Size of remote file:
- 4.55 GB
- SHA256:
- c65612cf515cf84e59498241de7d7e0235bff6e5f3910ea262007af8ac109d39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.