Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
decision-model
calibration
system-1
local
Eval Results
Instructions to use jeff-legacy/Jeff-Qwen3.5-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeff-legacy/Jeff-Qwen3.5-0.8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="jeff-legacy/Jeff-Qwen3.5-0.8B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("jeff-legacy/Jeff-Qwen3.5-0.8B") model = AutoModel.from_pretrained("jeff-legacy/Jeff-Qwen3.5-0.8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Typed Decisions benchmark results
#3
by codelion - opened
Adds this model's scores from the Typed Decisions leaderboard (LocalLLaMA/typed-decisions, test split, 400 cases and 2,000 decisions), so they appear on the benchmark's Hub leaderboard.
Zero-shot (not trained on the benchmark's train split). Scored by the benchmark maintainers on the test split.
Source and full table: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
This is a community-provided PR. If you would rather not list the model, or a number looks wrong, close the PR or tell us and we will fix it.