Instructions to use Jazhyc/aletheias-phoenix-blind-reasoning-r16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jazhyc/aletheias-phoenix-blind-reasoning-r16 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "Jazhyc/aletheias-phoenix-blind-reasoning-r16") - Notebooks
- Google Colab
- Kaggle
Phoenix Wright 5.1 main adapter
Rank-16, alpha-32 Qwen3.5-9B LoRA trained for one epoch on 2,875 parsed
varied-deception reasoning traces produced by GPT-OSS-120B. The teacher was
ground-truth blind: it saw only the observable conversation and final output,
generated a concise material-claim rationale, and supplied its own binary
decision. Its mistakes were retained rather than corrected or filtered by
ground-truth labels. Training used AdamW at 5e-5 with effective batch size 32.
The submission renders observable conversation and output only, disables Qwen
thinking, and normalizes the next-token logits for literal 0|1 directly after
Prediction: without generating the learned rationale. The adapter is intended
for the matching competition notebook and is not a general-purpose truth
classifier.
The adapter_model.safetensors SHA-256 is
bfe4a35ceb11055e7b7e77d29cc7027ccb5daa355703d9acd4d14eb03db01931.
The deployed Hugging Face repository is
Jazhyc/aletheias-phoenix-blind-reasoning-r16; the initial upload commit is
eaeb37ce03d791db340114eb9132494d7081d31f.
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