Instructions to use NbAiLabArchive/test_NCC_small_flax_stream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_small_flax_stream with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_small_flax_stream")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_small_flax_stream") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_small_flax_stream", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLabArchive/test_NCC_small_flax_stream: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLabArchive/test_NCC_small_flax_stream/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLabArchive/test_NCC_small_flax_stream/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLabArchive/test_NCC_small_flax_stream/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 670c64b0a13eebd1224c76ddddf875533cf88e7709b9ddaf9ac7d54730fbe3a1
- Size of remote file:
- 499 MB
- SHA256:
- 8bd7087cb1fb5fadf736fef3bef8511efb7cac943407e8984a0a0dac7606b1af
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