Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-afr-100steps-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-afr-100steps-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-afr-100steps-small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-afr-100steps-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download afr-2gpu-100steps.bin from jumelet/gptbert-afr-100steps-small: direct link, hf CLI and curl.
- Browser
- Download file 145 MB
-
https://huggingface.co/jumelet/gptbert-afr-100steps-small/resolve/main/afr-2gpu-100steps.bin
- Command line
-
hf download hf://jumelet/gptbert-afr-100steps-small/afr-2gpu-100steps.bin
-
curl -L -o afr-2gpu-100steps.bin https://huggingface.co/jumelet/gptbert-afr-100steps-small/resolve/main/afr-2gpu-100steps.bin
145 MB
- Xet hash:
- 4060cea99f8e3d442acc6980aeedbe35268cb3ed24e82403156c2c6953779b5f
- Size of remote file:
- 145 MB
- SHA256:
- 55a95e9e8bc0a9e0580776b7347ecf41a948fea07498d16aafc1e6fc8ee70f4b
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