Text Classification
Transformers
Safetensors
English
qwen3_5_text
feature-extraction
darwin
ztc
zero-token-classifier
decision-engine
system-one
s1mb
calibration
probabilistic-classification
Instructions to use FINAL-Bench/Darwin-27B-ZTC-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-27B-ZTC-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINAL-Bench/Darwin-27B-ZTC-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-ZTC-v2") model = AutoModel.from_pretrained("FINAL-Bench/Darwin-27B-ZTC-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K