Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
jeff
decision-model
calibration
system-1
local
Instructions to use mstrasser/jeff-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mstrasser/jeff-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="mstrasser/jeff-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mstrasser/jeff-base") model = AutoModel.from_pretrained("mstrasser/jeff-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mstrasser/jeff-base: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/mstrasser/jeff-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://mstrasser/jeff-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mstrasser/jeff-base/resolve/main/tokenizer.json
20 MB
- Xet hash:
- a534f7c9d12bb01a2bc21781b55369e077043baed4c8f646fdeff5ff02dfd4d6
- Size of remote file:
- 20 MB
- SHA256:
- 6f32ce20dc35f57a7f9ad1eac03525bd7d30f9df8cea6507e958279cc3657706
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