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06_double_dataset – AI Model by XKilin | AlphaNeural AI

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XKilin/
06_double_dataset

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structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v2

u-10bei/structured_data_with_cot_dataset_512_v4

Qwen/Qwen3-4B-Instruct-2507

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qwen3-4b-structured-output-lora-v2v4:LR(1e-04),EP(2),SEQ(768)

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 Data

Combined dataset (deduplicated):

u-10bei/structured_data_with_cot_dataset_512_v2 (3,933 samples)
u-10bei/structured_data_with_cot_dataset_512_v4 (4,464 samples after dedup)
Total: ~8,397 unique samples

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 768
Epochs: 2
Learning rate: 1e-04
LoRA: r=128, alpha=256

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
u-10bei/structured_data_with_cot_dataset_512_v4

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