Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
text
custom_code
Instructions to use PantagrueLLM/text-base-wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/text-base-wiki with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/text-base-wiki", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/text-base-wiki", trust_remote_code=True, dtype="auto") - Fairseq
How to use PantagrueLLM/text-base-wiki 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-wiki" ) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:44d17393282f9ea57362190fa40f80e5810b271f95ce9cdbe1d5f771fde526b2
|
| 3 |
+
size 498941730
|