Instructions to use hjpark10/jeol_custom_sampler_batch_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hjpark10/jeol_custom_sampler_batch_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hjpark10/jeol_custom_sampler_batch_8")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hjpark10/jeol_custom_sampler_batch_8") model = AutoModel.from_pretrained("hjpark10/jeol_custom_sampler_batch_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hjpark10/jeol_custom_sampler_batch_8: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/hjpark10/jeol_custom_sampler_batch_8/resolve/main/tokenizer.json
- Command line
-
hf download hf://hjpark10/jeol_custom_sampler_batch_8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hjpark10/jeol_custom_sampler_batch_8/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5d747ca97fe6e145aef974d51b6921c06a4eed57a0513eb2427235a9af134752
- Size of remote file:
- 17.1 MB
- SHA256:
- e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
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