Instructions to use EvoLenTokenizer/base-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EvoLenTokenizer/base-100k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="EvoLenTokenizer/base-100k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EvoLenTokenizer/base-100k") model = AutoModelForMaskedLM.from_pretrained("EvoLenTokenizer/base-100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card with metadata and links
#1
by nielsr HF Staff - opened
This PR improves the auto-generated model card by:
- Adding relevant metadata:
license,library_name, andpipeline_tag. - Adding a link to the paper and the project’s GitHub repository.
- Replacing placeholder text with a concise description of the model, its intended uses, limitations, and training/evaluation details.
nancyH changed pull request status to merged