Instructions to use Synthyra/Profluent-E1-600M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/Profluent-E1-600M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Synthyra/Profluent-E1-600M", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Synthyra/Profluent-E1-600M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 336 Bytes
4723b99 faef572 4723b99 | 1 2 3 4 5 6 7 8 9 10 11 12 | """Lazy model-family namespace for FastPLMs.
Model classes are resolved through Transformers AutoClasses and the typed
registry. Importing this package therefore does not load checkpoints, create
tokenizers, compile kernels, or initialize an accelerator runtime.
"""
from __future__ import annotations
__all__: tuple[str, ...] = ()
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