Instructions to use Edward1239/DL_hw3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Edward1239/DL_hw3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "Edward1239/DL_hw3") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen2.5-14B-Instruct | |
| library_name: peft | |
| pipeline_tag: text-generation | |
| tags: | |
| - qwen | |
| - grpo | |
| - lora | |
| - peft | |
| - multiple-choice | |
| # DL HW3 GRPO LoRA Adapter | |
| This repository contains the final LoRA adapter for DL HW3: Reasoning LLM Step 3 with GRPO. | |
| ## Base Model | |
| Qwen/Qwen2.5-14B-Instruct | |
| ## Method | |
| The model was initialized from my Step2 SFT LoRA adapter and further optimized using GRPO. | |
| ## Final Adapter | |
| outputs/grpo_hw2best_balanced_30steps_lr2e8 | |
| ## Training Data | |
| The final GRPO run used a balanced version of HW2_.csv: | |
| - A: 261 | |
| - B: 261 | |
| - C: 261 | |
| - D: 261 | |
| ## Inference | |
| Final inference uses score-only A/B/C/D log-softmax scoring. | |
| ## Public Leaderboard | |
| Public LB score: around 0.71 | |