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qwen3-4b-structured-output-lora-comp-2026feb-run09 – AI Model by yamayos | AlphaNeural AI

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yamayos/
qwen3-4b-structured-output-lora-comp-2026feb-run09

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Model Card for structeval RUN09

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_113922_3a8b212d

Learning rate: 2.4e-6
Epochs: 2
Max seq length: 1536
Batch size: 2
Gradient accumulation: 4

LoRA: r = 64
alpha = 128
dropout = 0.0

Upsampling: {"toml": 3.0}

Validation loss: 0.9636380076408386

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-run09"

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

tokenizer = AutoTokenizer.from_pretrained(base)