Instructions to use lilyzhng/qwen3.5-9b-tau2-retail-sft-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-retail-sft-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-retail-sft-lora") - Notebooks
- Google Colab
- Kaggle
File size: 587 Bytes
1fca5f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ---
base_model: Qwen/Qwen3.5-9B
library_name: peft
tags:
- tau2-bench
- qwen3.5
- lora
---
# Qwen3.5-9B τ²-bench SFT LoRA
PEFT adapter only — base weights stay on `Qwen/Qwen3.5-9B`.
| Field | Value |
| --- | --- |
| TRAIN_MODE | `exp` |
| LoRA rank | 32 |
| W&B | [https://wandb.ai/alchemxz/decagon-posttraining-sft/runs/bsmjocsv](https://wandb.ai/alchemxz/decagon-posttraining-sft/runs/bsmjocsv) |
## vLLM (no merge upload)
```bash
vllm serve Qwen/Qwen3.5-9B --enable-lora --lora-modules sft=lilyzhng/qwen3.5-9b-tau2-retail-sft-lora \
--max-lora-rank 32 --dtype bfloat16
```
|