perryhsb/structeval-qwen3-4b-lora

StructEval Structured Output LoRA

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).

The model outputs structured data directly without Chain-of-Thought reasoning, reducing parse failures and token waste.

Training Data

Data sources:

• u-10bei/structured_data_with_cot_dataset_512_v4
• u-10bei/structured_data_with_cot_dataset_512_v5
• daichira/structured-hard-sft-4k
• daichira/structured-5k-mix-sft
• daichira/structured-3k-mix-sft
• u-10bei/structured_data_with_cot_dataset_512_v2
• Rule-based conversion pair augmentation

Total: ~30,000 samples after augmentation, cleaning, and rebalancing.

Data augmentation (non-LLM, rule-based):

• CoT extraction: removed reasoning text, kept structured output only
• Rule-based format conversion pairs between all 5 formats
• Gap filling for underrepresented task types (Text-to-XML, YAML-to-XML)
• Random structure generation for diversity
• Rebalancing to match evaluation distribution

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit) via Unsloth
• Max sequence length: 4096
• Epochs: 2
• Learning rate: 2e-5 (cosine schedule, warmup 10%)
• LoRA: r=128, alpha=256
• Target modules: q/k/v/o/gate/up/down projections
• Batch size: 2 x 8 gradient accumulation = effective 16
• Loss: assistant-only (user/system prompts masked)

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "perryhsb/structeval-qwen3-4b-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

All training data sourced from permitted datasets listed in the competition rules. Non-LLM augmentation methods only (regex, format parsers, rule-based conversion).