Feature Extraction
Transformers
Safetensors
Fairseq
French
pantagruel_uni
fill-mask
data2vec2
JEPA
text
custom_code
Instructions to use PantagrueLLM/Text_Base_FR_croissant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/Text_Base_FR_croissant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/Text_Base_FR_croissant", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("PantagrueLLM/Text_Base_FR_croissant", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/Text_Base_FR_croissant with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "PantagrueLLM/Text_Base_FR_croissant" ) - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload folder using huggingface_hub
Verified signature23ab6c6
-
Update README.md
Verified signaturee8d86a4
-
Update README.md
Verified signaturecd1a422 -
Create README.md
Verified signaturef45f163 -
Upload folder using huggingface_hub
Verified signature4b954d1 -
Upload folder using huggingface_hub
Verified signature3ee497a
-
Upload folder using huggingface_hub
Verified signature0ddc28e -
initial commit
Verified signature84fabc7