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.

qwen3-4b-structured-output-lora-v12 – AI Model by melon1891 | AlphaNeural AI

You can deploy this model and start earning money today!

melon1891/
qwen3-4b-structured-output-lora-v12

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-v12

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: 768
Epochs: 2
Learning rate: 2e-05
LoRA: r=4, alpha=4

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/your-repo"

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 (MIT License) - 2,770 samples after deduplication

Preprocessing applied:

Removed 1,838 exact duplicate samples (4,608 → 2,770)
Normalized CoT format ("Output:" marker standardization)

Training notes:

CoT masking enabled (Output: marker)
LoRA r=4, alpha=4

Compliance: Users must comply with the MIT license and the base model's original terms of use.