Feature Extraction
Transformers
Safetensors
English
matilda_jev
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/matilda-jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/matilda-jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 280 Bytes
d10ad42 | 1 2 3 4 5 6 7 8 9 10 | """MATILDA tokenizer alias: vocabulary, normalization and special tokens are unchanged."""
from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer
class MatildaJevTokenizer(Qwen2Tokenizer):
pass
MatildaJevTokenizer.register_for_auto_class("AutoTokenizer")
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