metadata
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- unsloth
- structured-output
- structeval
qwen3-4b-structured-output-lora
This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit) with 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 output (assistant-only loss).
Chain-of-Thought masking: Enabled
Learning mode: after_marker
Data Preprocessing
Rule-based normalization was applied before training:
- Extracting content after output markers
- Removing code fences (
json /yaml /xml /toml) - Removing leading boilerplate and trailing notes
- Recursive JSON exact-match deduplication
Dedupe enabled: Yes
Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit) + Unsloth
- Max sequence length: 1024
- Epochs: 1
- Learning rate: 3e-05
- Warmup ratio: 0.06
- Weight decay: 0.02
- LoRA: r=48, alpha=96, dropout=0.06
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "tropico0313/my-lora-test"
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 (IMPORTANT)
Training dataset: u-10bei/structured_data_with_cot_dataset_512_v2
Dataset License: MIT License.
Users must comply with the MIT license (including copyright notice)
and the base model's original terms of use.