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
Download .eval_results/typed-decisions.yaml from FINAL-Bench/Darwin-27B-ZTC: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC/resolve/main/.eval_results/typed-decisions.yaml
- Command line
-
hf download hf://FINAL-Bench/Darwin-27B-ZTC/.eval_results/typed-decisions.yaml
-
curl -L -o typed-decisions.yaml https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC/resolve/main/.eval_results/typed-decisions.yaml
2.1 kB
| - dataset: | |
| id: LocalLLaMA/typed-decisions | |
| task_id: accuracy | |
| value: 0.743 | |
| date: '2026-10-08' | |
| source: | |
| url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions | |
| name: Typed Decisions | |
| user: SeaWolf-AI | |
| notes: 'General, zero-shot: never trained on the Typed Decisions train split, its workflows or its question schemas. Test split, 400 cases, 2,000 decisions, one request per case with the state and all five questions (README request shape), all answered, zero errors. Accuracy = agreement with the gold label; KL = KL(gold || prediction); Brier summed over options (scorer reproduces the README Uniform reference). One forward pass per question, no generated tokens.' | |
| - dataset: | |
| id: LocalLLaMA/typed-decisions | |
| task_id: kl_from_gold | |
| value: 0.204 | |
| date: '2026-10-08' | |
| source: | |
| url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions | |
| name: Typed Decisions | |
| user: SeaWolf-AI | |
| notes: 'General, zero-shot: never trained on the Typed Decisions train split, its workflows or its question schemas. Test split, 400 cases, 2,000 decisions, one request per case with the state and all five questions (README request shape), all answered, zero errors. Accuracy = agreement with the gold label; KL = KL(gold || prediction); Brier summed over options (scorer reproduces the README Uniform reference). One forward pass per question, no generated tokens.' | |
| - dataset: | |
| id: LocalLLaMA/typed-decisions | |
| task_id: brier | |
| value: 0.097 | |
| date: '2026-10-08' | |
| source: | |
| url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions | |
| name: Typed Decisions | |
| user: SeaWolf-AI | |
| notes: 'General, zero-shot: never trained on the Typed Decisions train split, its workflows or its question schemas. Test split, 400 cases, 2,000 decisions, one request per case with the state and all five questions (README request shape), all answered, zero errors. Accuracy = agreement with the gold label; KL = KL(gold || prediction); Brier summed over options (scorer reproduces the README Uniform reference). One forward pass per question, no generated tokens.' | |