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hiro0904/
structeval2026-sft-u10b1-1-nomarker-nocot-lr3em6

Name: structeval2026-sft-u10b1-1-nomarker-nocot-lr3em6
Availability: InStock

hiro0904/1-1-v2-cleaned-no-cot

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

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structeval2026--sft-u10b1-1-nomarker-nocot-lr3em6

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 answer tokens, while reasoning tokens before markers such as "Output:" are masked.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 1
Learning rate: 3e-06
LoRA: r=64, alpha=128

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "hiro0904/structeval2026-sft-u10b1-1-nomarker-nocot-lr3em6"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data: hiro0904/1-1-v2-cleaned-no-cot

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.