Instructions to use zhenchonghu/dsaa6000q-a3-forward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zhenchonghu/dsaa6000q-a3-forward 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-forward") - Notebooks
- Google Colab
- Kaggle
DSAA6000Q Forward 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
Forward instruction-following model trained on the curated self-alignment dataset.
Base model
Qwen/Qwen3-1.7B
Training data
- Dataset repo:
zhenchonghu/dsaa6000q-a3-curated - Local curated file:
/kaggle/working/dsaa6000q_outputs/dsaa6000q_a3/curated/curated_pairs.jsonl - Training rows: 1
Notes
- This is a PEFT LoRA adapter, not a fully merged checkpoint.
- The model was trained with a standalone Kaggle-compatible notebook.
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