You-En/NYCU_IAlI_DL2026_LLM2_SFT_with_Reasoning_Data

NYCU-IAII-DL2026 LLM2 SFT with Reasoning

This repository contains the LoRA adapter trained for the NYCU-IAII-DL2026 LLM #2 task: Reasoning LLM SFT with Reasoning Information.

The adapter is fine-tuned from:

Qwen/Qwen2.5-7B-Instruct

This repository does not contain a full merged model. It contains a PEFT / LoRA adapter that should be loaded on top of the base model.

Model Details

• —Base model: Qwen/Qwen2.5-7B-Instruct
• —Fine-tuning method: Supervised fine-tuning with reasoning information
• —Adapter method: LoRA / PEFT
• —Quantization during training: 4-bit quantization
• —Framework: PyTorch, Transformers, PEFT
• —Task type: Multiple-choice question answering with reasoning information
• —Expected output: one of A, B, C, or D

Files

This repository includes:

adapter_config.json
adapter_model.safetensors
tokenizer.json
tokenizer_config.json
chat_template.jinja
README.md

Limitations

This adapter is trained specifically for the course multiple-choice reasoning task. It may not generalize well to open-ended reasoning or general chat scenarios.