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shibatake-SFT_2602050153 – AI Model by takeofuture | AlphaNeural AI

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takeofuture/
shibatake-SFT_2602050153

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structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v5

Qwen/Qwen3-4B-Instruct-2507

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qwen3-4b-structured-output-lora-shibatake-v3

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 output, while intermediate reasoning (Chain-of-Thought) is masked.

CoT mask: 1
Learn mode: after_marker
Output markers: Output:,OUTPUT:,Final:,Answer:,Result:,Response:

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit, Unsloth)
Max sequence length: 512
Epochs: 4
Learning rate: 2e-05
LoRA: r=32, alpha=64, dropout=0.05

Usage

pip install -U transformers peft accelerate safetensors

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "takeofuture/shibatake-SFT_2602050153"  # ✅ this repo id

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v5 Dataset License: MIT License Compliance: Users must comply with the dataset license (including copyright notice) and the base model's original terms of use.

Last updated: 2026-02-05 01:53:41 UTC