Instructions to use Narsil/small_summarization_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/small_summarization_test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Narsil/small_summarization_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Narsil/small_summarization_test: direct link, hf CLI and curl.
- Browser
- Download file 518 Bytes
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https://huggingface.co/Narsil/small_summarization_test/resolve/main/README.md
- Command line
-
hf download hf://Narsil/small_summarization_test/README.md
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curl -L -o README.md https://huggingface.co/Narsil/small_summarization_test/resolve/main/README.md
518 Bytes
import tempfile
from tokenizers import Tokenizer, models
from transformers import PreTrainedTokenizerFast
model_max_length = 4
vocab = [(chr(i), i) for i in range(256)]
tokenizer = Tokenizer(models.Unigram(vocab))
with tempfile.NamedTemporaryFile() as f:
tokenizer.save(f.name)
real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, model_max_length=model_max_length)
real_tokenizer._tokenizer.save("dummy/tokenizer.json")
config uses Albert which works with a minimal config.json