Instructions to use hf-tiny-model-private/tiny-random-XLMWithLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLMWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-XLMWithLMHeadModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-XLMWithLMHeadModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-XLMWithLMHeadModel: direct link, hf CLI and curl.
- Browser
- Download file 4.33 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLMWithLMHeadModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLMWithLMHeadModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-XLMWithLMHeadModel/resolve/main/pytorch_model.bin
4.33 MB
- Xet hash:
- bd66a28260950a357df2e57a53193f957532f9d21ea0a30eaadd5f327a1ae687
- Size of remote file:
- 4.33 MB
- SHA256:
- d7c68fa4b5a3db1c5339a1e75fb823248695b54a1a7c21c4560c904fc713bc77
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