Instructions to use hf-internal-testing/tiny-random-GPTBigCodeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTBigCodeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-GPTBigCodeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab1ee17f7466a8c012c3fab13b56ba9738a313abfbac3e00bddd72ca4ab79d29
- Size of remote file:
- 622 kB
- SHA256:
- 62c42326c4775bb9331d56067a78471827951b3ba0728659e6c002aad4fb00af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.