Text Classification
Transformers
Safetensors
English
qwen3_5_text
feature-extraction
darwin
ztc
zero-token-classifier
decision-engine
typed-decisions
system-one
calibration
probabilistic-classification
Eval Results
Instructions to use FINAL-Bench/Darwin-27B-ZTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-27B-ZTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINAL-Bench/Darwin-27B-ZTC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-ZTC") model = AutoModel.from_pretrained("FINAL-Bench/Darwin-27B-ZTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
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