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dkl4/llm2025-qwen3-4b-structured-output-lora-01-v5

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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llm2025-qwen3-4b-structured-output-lora-01-v5

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

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

base_model: Qwen/Qwen3-4B-Instruct-2507 datasets:

—u-10bei/structureddatawithcotdataset512v2 language:
—en license: Apache-2.0 libraryname: peft pipelinetag: text-generation tags:
—qlora
—lora
—structured-output ---

Training Objective

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

Loss is applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) is masked.

Training Configuration

—Base model: unsloth/Qwen3-4B-Instruct-2507
—Method: QLoRA (4-bit)
—Max sequence length: 512
—Epochs: 3
—Learning rate: 3e-06
—LoRA: r=64, alpha=128

Usage

python

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

base = "unsloth/Qwen3-4B-Instruct-2507"
adapter = "dkl4/llm2025-qwen3-4b-structured-output-lora-01-v5"

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/structureddatawithcotdataset512v5

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