Feature Extraction
Transformers
PyTorch
Safetensors
Fairseq
French
pantagruel_uni
data2vec2
JEPA
speech
custom_code
Instructions to use PantagrueLLM/speech-large-14K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PantagrueLLM/speech-large-14K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PantagrueLLM/speech-large-14K", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PantagrueLLM/speech-large-14K", trust_remote_code=True, device_map="auto") - Fairseq
How to use PantagrueLLM/speech-large-14K 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/speech-large-14K" ) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a02a35d1551cc844aa9a85823cd9e38656fe161ebb377bafda4d5e7324426769
|
| 3 |
+
size 1253137888
|