Instructions to use EvoLenTokenizer/base-200k-sft-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EvoLenTokenizer/base-200k-sft-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EvoLenTokenizer/base-200k-sft-checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EvoLenTokenizer/base-200k-sft-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add library_name and pipeline_tag to model card metadata
#1
by nancyH - opened
Brings this repo in line with the other EvoLen checkpoint repos. text-classification is used because these are per-task fine-tuned sequence-classification checkpoints.
nancyH changed pull request status to merged