Instructions to use zhenchonghu/dsaa6000q-a3-backward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zhenchonghu/dsaa6000q-a3-backward with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "zhenchonghu/dsaa6000q-a3-backward") - Notebooks
- Google Colab
- Kaggle
DSAA6000Q Backward LoRA Adapter
This repository contains a LoRA adapter produced for DSAA6000Q Assignment 3 using the self-alignment pipeline from Self Alignment with Instruction Backtranslation.
Summary
Backward model p(instruction | response) trained on OpenAssistant Guanaco.
Base model
Qwen/Qwen3-1.7B
Training data
- Dataset:
timdettmers/openassistant-guanacosplittrain - Max train samples: 2000
- Max eval samples: 200
Notes
- This is a PEFT LoRA adapter, not a fully merged checkpoint.
- The model was trained with a standalone Kaggle-compatible notebook.
- Downloads last month
- -