Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
maincode
matilda
custom_code
Instructions to use Maincode/matilda-decision-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-decision-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/matilda-decision-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/matilda-decision-v1", trust_remote_code=True) model = AutoModel.from_pretrained("Maincode/matilda-decision-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Maincode/matilda-decision-v1: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Maincode/matilda-decision-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://Maincode/matilda-decision-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Maincode/matilda-decision-v1/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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