Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

hoge20260202 – AI Model by ryo25 | AlphaNeural AI

You can deploy this model and start earning money today!

ryo25/
hoge20260202

like

0

structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v2

Qwen/Qwen3-4B-Instruct-2507

Views

No views yet

Qwen3-4B Structured Output LoRA (LLM2025 Final Assignment)

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).

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

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 1
Learning rate: 1e-06
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 = "ryo25/hoge20260202"

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

Sources & License

Training dataset: u-10bei/structured_data_with_cot_dataset_512_v2
Dataset license: Please refer to the original dataset page on Hugging Face.
Base model license: Apache-2.0
Adapter license: Apache-2.0

Users must comply with the dataset’s original license terms and the base model’s terms of use.