YoungerWu/11667-mcpdial-best-checkpoints

MCPDial Best Checkpoints

This repository contains the best performing checkpoints from hyperparameter search experiments.

Models

Llama (llama_lr_3e5_r48_a16)

• BLEU: 0.0586
• ROUGE-1: 0.2939
• ROUGE-L: 0.2163
• Distinct-1/2: 0.2199 / 0.6315
• Average Length: 34.6 tokens
• Hyperparameters:
  • Learning Rate: 3e-5
  • LoRA Rank: 48
  • LoRA Alpha: 16

Qwen (qwen_lr_1e5_r128_a32)

• BLEU: 0.0182
• ROUGE-1: 0.1577
• ROUGE-L: 0.1074
• Distinct-1/2: 0.0988 / 0.3736
• Average Length: 176.8 tokens
• Hyperparameters:
  • Learning Rate: 1e-5
  • LoRA Rank: 128
  • LoRA Alpha: 32

Usage

Load the adapters using PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# For Llama
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "YoungerWu/11667-mcpdial-best-checkpoints", subfolder="llama_lr_3e5_r48_a16")

# For Qwen
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "YoungerWu/11667-mcpdial-best-checkpoints", subfolder="qwen_lr_1e5_r128_a32")

Project

Part of CMU 11-667 Final Project on multi-turn dialogue generation.