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
multimodal
vision
computer-use
robotics
Eval Results (legacy)
Instructions to use autotrust/AutoTrust-Decider-Mini-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/AutoTrust-Decider-Mini-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/AutoTrust-Decider-Mini-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/AutoTrust-Decider-Mini-9B") model = AutoModelForCausalLM.from_pretrained("autotrust/AutoTrust-Decider-Mini-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download reports/fanout_pc.json from autotrust/AutoTrust-Decider-Mini-9B: direct link, hf CLI and curl.
- Browser
- Download file 424 Bytes
-
https://huggingface.co/autotrust/AutoTrust-Decider-Mini-9B/resolve/main/reports/fanout_pc.json
- Command line
-
hf download hf://autotrust/AutoTrust-Decider-Mini-9B/reports/fanout_pc.json
-
curl -L -o fanout_pc.json https://huggingface.co/autotrust/AutoTrust-Decider-Mini-9B/resolve/main/reports/fanout_pc.json
424 Bytes
| { | |
| "prefix_caching": true, | |
| "states": 293, | |
| "questions": 2269, | |
| "prompt_tokens": 1095592, | |
| "one_call": { | |
| "s": 18.364436388015747, | |
| "rows_per_s": 123.55402322505812, | |
| "cached_tokens": 214368, | |
| "cached_frac": 0.19566407932880123 | |
| }, | |
| "two_phase": { | |
| "s": 17.282485008239746, | |
| "rows_per_s": 131.28898991772377, | |
| "cached_tokens": 214368, | |
| "cached_frac": 0.19566407932880123, | |
| "rest_rows_per_s": 137.01403920545346 | |
| } | |
| } |