debaterhub/sentence-selection-orpo-v3

Sentence Selection ORPO v3

LoRA adapter for debate card cutting / sentence selection task.

Model Description

This is an ORPO-trained LoRA adapter for selecting relevant sentences from evidence to support debate claims. The model was trained on 507 DPO pairs using ORPO (Odds Ratio Preference Optimization).

Base Model: Qwen/Qwen3-30B-A3B (with SFT fine-tuning)

Training Details

• —Method: ORPO (Odds Ratio Preference Optimization)
• —Training Data: 507 DPO pairs (456 train, 51 validation)
• —Learning Rate: 5e-7
• —LoRA Rank: 32
• —LoRA Alpha: 64
• —Target Modules: qproj, kproj, vproj, oproj
• —Epochs: ~0.35 (checkpoint at step 40)

Training Metrics

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "debaterhub/sentence-selection-orpo-v3")

Task Format

Input format:

Select sentences supporting:
Claim: [claim text]

TEXT ([citation]):
[1] First sentence.
[2] Second sentence.
...

Expected output:

Selected IDs: [1, 3, 5]

License

Apache 2.0

Framework Versions

• —PEFT 0.15.2
• —Transformers 4.57.3
• —PyTorch 2.9.0