Instructions to use lilyzhng/qwen3.5-9b-tau2-opd-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lilyzhng/qwen3.5-9b-tau2-opd-lora 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, "lilyzhng/qwen3.5-9b-tau2-opd-lora") - Notebooks
- Google Colab
- Kaggle
Qwen3.5-9B τ²-bench OPD LoRA (GKD)
PEFT adapter from on-policy distillation — base weights stay on Qwen/Qwen3.5-9B.
| Field | Value |
|---|---|
| TRAIN_MODE | exp_fast |
| LoRA rank | 32 |
| W&B | https://wandb.ai/alchemxz/decagon-posttraining-opd/runs/cibzral8 |
vLLM (runtime merge)
vllm serve Qwen/Qwen3.5-9B --enable-lora --lora-modules opd=lilyzhng/qwen3.5-9b-tau2-opd-lora \
--max-lora-rank 32 --dtype bfloat16
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