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NobutaMN/
qwen3-4b-structeval-lora-v2change-sft7000-run7

NobutaMN/qwen3-4b-structeval-merged-v2change

adapter

apache-2.0

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qwen3-4b-structeval-lora-v2change-sft7000-run7

This repository provides a LoRA adapter fine-tuned from
NobutaMN/qwen3-4b-structeval-merged-v2change 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, focusing on format adherence and structural correctness.

It aims to reduce format breakage in model outputs across common structured formats: JSON / YAML / XML / TOML / CSV.

Loss application

Loss is applied only to the final assistant output (assistant-only loss)
Prompt, system, and user context tokens are excluded from loss computation

Chain-of-Thought handling

Chain-of-Thought is masked
Learning focuses only on content generated after explicit output markers
(e.g., Output:, OUTPUT:, Final:, Answer:, Result:, Response:)

Training Configuration

Base model: NobutaMN/qwen3-4b-structeval-merged-v2change
Method: QLoRA (4-bit, Unsloth)
Max sequence length: 512
Epochs: 1
Learning rate: 3e-6
Weight decay: 0.05
Effective batch size: 16
(per_device_train_batch_size=2 × gradient_accumulation_steps=8)
Validation split: 0.05
Seed: 3407
LoRA: r=64, alpha=128
Output learning mode: after_marker
Chain-of-Thought mask: enabled

Language

English
Inputs and outputs in the training data are primarily English structured text

Usage

Device

GPU recommended (Colab / A100 / T4 class GPUs)

Precision

bfloat16 (bf16) is recommended when supported
Falls back to float16 (fp16) when bf16 is not available

python

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

base = "NobutaMN/qwen3-4b-structeval-merged-v2change"
adapter = "your_id/qwen3-4b-structeval-lora-v2change-sft7000-run7"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.bfloat16
    if torch.cuda.is_available() and torch.cuda.is_bf16_supported()
    else torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(model, adapter)

NobutaMN/qwen3-4b-structeval-lora-v2change-sft7000-run7