Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
system-two
blocks-of-experts
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
Eval Results (legacy)
Eval Results
Instructions to use autotrust/AutoTrust-Decider-Pro-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/AutoTrust-Decider-Pro-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/AutoTrust-Decider-Pro-27B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/AutoTrust-Decider-Pro-27B") model = AutoModelForCausalLM.from_pretrained("autotrust/AutoTrust-Decider-Pro-27B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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serve_decide.py: System 1 only by default for every model (thinking is opt-in)
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serve_decide.py: one file for JEV-27B and GEV; optional adaptive thinking (thinking=auto|on, reasoning_effort, think_budget; off by default for JEV-27B), answer read-out that also works with speculative decoding
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System 1 over HTTP (POST /v1/decide, serve_decide.py), choice up to 256 options, prompt guide, 256K context test
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