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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/qwen3-4b-structured-output-lora-v5.4"

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

Note

This model builds upon v5.2 with targeted fixes for:

TOML inline table syntax
TOML array table syntax
XML special character escaping

kmd2525/qwen3-4b-structured-output-lora-v5.4

qwen3-4b-structured-output-lora-v5.4

Strategy A: v5.2 Base + Low LR Targeted Learning

Training Details

Usage

Note

kmd2525/
qwen3-4b-structured-output-lora-v5.4