base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- daichira/structured-hard-sft-4k
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
- supervised-finetuning
- assistant-only-loss
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.
Overview
This adapter is designed to improve structured output reliability
across formats such as:
- JSON
- YAML
- XML
- TOML
- CSV
The focus is on increasing formatting stability, bracket correctness, key-value alignment, and structural consistency.
Training Objective
The model is trained via Supervised Fine-Tuning (SFT) with the following design:
- Loss is applied only to the final assistant output
- Intermediate reasoning (Chain-of-Thought) is masked
- Training emphasizes output formatting precision
This setup prioritizes structured output correctness over verbose reasoning.
Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit)
- Max sequence length: 1024
- Epochs: 3
- Learning rate: 2e-05
- LoRA configuration:
- r = 128
- alpha = 256
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/your-repo"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
base,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()
Sources & Terms (IMPORTANT)
Training data: daichira/structured-hard-sft-4k
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.