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qwen3-4b-structured-output-lora-comp-2026feb-run10 – AI Model by yamayos | AlphaNeural AI

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yamayos/
qwen3-4b-structured-output-lora-comp-2026feb-run10

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Model Card for structeval RUN10

This adapter was trained for the StructEval competition using supervised fine-tuning (SFT).

Base model

unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit

Training configuration

RUN_ID: structeval_20260301_124427_3a8b212d
Learning rate: 2.8e-6
Epochs: 2
Max seq length: 1536
Batch size: 2
Gradient accumulation: 4
LoRA: r=64, alpha=128, dropout=0.0
Upsampling: {"toml": 4.0, "yaml": 2.0, "xml": 2.0}
Validation loss: 0.9347118735313416

Purpose

This LoRA adapter was trained to improve structured output generation (JSON / YAML / TOML / XML / CSV) for the StructEval competition.

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit"
adapter = "yamayos/qwen3-4b-structured-output-lora-comp-2026feb-run10"

model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, adapter)

tokenizer = AutoTokenizer.from_pretrained(base)