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)# 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
Download tokenizer.json from Maincode/matilda-jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://Maincode/matilda-jev-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Maincode/matilda-jev-v1/resolve/main/tokenizer.json
20 MB
- Xet hash:
- a534f7c9d12bb01a2bc21781b55369e077043baed4c8f646fdeff5ff02dfd4d6
- Size of remote file:
- 20 MB
- SHA256:
- 6f32ce20dc35f57a7f9ad1eac03525bd7d30f9df8cea6507e958279cc3657706
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