Instructions to use geekfeed/gpt2_ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use geekfeed/gpt2_ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="geekfeed/gpt2_ja")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("geekfeed/gpt2_ja") model = AutoModel.from_pretrained("geekfeed/gpt2_ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from geekfeed/gpt2_ja: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/geekfeed/gpt2_ja/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://geekfeed/gpt2_ja/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/geekfeed/gpt2_ja/resolve/main/flax_model.msgpack
442 MB
- Xet hash:
- 0965949f7b69fe332708fa533c42bbf7a4866d7e04bdbf39174fc72421f2a371
- Size of remote file:
- 442 MB
- SHA256:
- dcf9978010448181fcbb69ac526bf4e95577171bfe1fad69dbbe682184e26ef1
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