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NYCU_IAlI_DL2026_LLM2_SFT_with_Reasoning_Data

Name: NYCU_IAlI_DL2026_LLM2_SFT_with_Reasoning_Data
Availability: InStock

supervised-finetuning

text-generation

nycu-iaii-dl2026

conversational

Qwen/Qwen2.5-7B-Instruct

adapter

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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:

text

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.

You-En/NYCU_IAlI_DL2026_LLM2_SFT_with_Reasoning_Data

NYCU-IAII-DL2026 LLM2 SFT with Reasoning

Model Details

Files

Limitations

You-En/
NYCU_IAlI_DL2026_LLM2_SFT_with_Reasoning_Data