You can deploy this model and start earning money today!

NTA2/
qwen3-4b-structured-lora

Name: qwen3-4b-structured-lora
Availability: InStock

u-10bei/structured_data_with_cot_dataset_512_v5

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

Views

No views yet

qwen3-4b-structured-output-lora (T4 Optimized)

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 Hyperparameters

Parameter	Value
Base Model	`Qwen/Qwen3-4B-Instruct-2507`
Dataset	`u-10bei/structured_data_with_cot_dataset_512_v5`
Method	`QLoRA (4-bit, Unsloth)`
LoRA Rank (r)	`128`
LoRA Alpha	`256`
LoRA Dropout	`0`
Target Modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Max Sequence Length	`1024`
Epochs	`2`
Batch Size	`1 (per device)`
Gradient Accumulation	`16`
Total Batch Size	`16`
Learning Rate	`2e-4`
Scheduler	`cosine`
Warmup Ratio	`0.1`
Weight Decay	`0.05`
Seed	`3407`
Optimizer	`AdamW (8-bit)`

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "NTA2/qwen3-4b-structured-lora"

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_v5

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.