lora-repo-5 (Mixed SFT)

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth).

This repository contains LoRA adapter weights only. The base model must be loaded separately.

Training Objective

This adapter is trained to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).

Dataset Strategy: Mixed training was performed to balance difficult logical tasks with basic CoT reasoning:

• u-10bei/structured_data_with_cot_dataset_512_v2

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 512
• Epochs: 2
• Learning rate: 2e-05
• LoRA: r=64, alpha=128

Usage

python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch

base = "Qwen/Qwen3-4B-Instruct-2507" adapter = "oretti/lora-repo-5 "

tokenizer = AutoTokenizer.from_pretrained(base) model = AutoModelForCausalLM.from_pretrained( base, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data:

• u-10bei/structured_data_with_cot_dataset_512_v2

Dataset License: MIT License. Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.