You can deploy this model and start earning money today!

Tentoumaru/
lora_structeval_t_unsloth_qwen3_4b_v2_v4_nocot

Name: lora_structeval_t_unsloth_qwen3_4b_v2_v4_nocot
Availability: InStock

Tentoumaru/structured_v2_v4_nocot

unsloth/Qwen3-4B-Instruct-2507

adapter

apache-2.0

Views

No views yet

＜unsloth_qwen3-4b-dataset_512_v2_v4_nocot＞

This repository provides a LoRA adapter fine-tuned from unsloth/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.

Training Configuration

Base model: unsloth/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 2048
Epochs: 1
Learning rate: 1e-06
LoRA: r=64, alpha=128

Usage

python

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

base = "unsloth/Qwen3-4B-Instruct-2507"
adapter = "Tentoumaru/structured_data_with_cot_dataset_512_v2_nocot"

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 data: Tentoumaru/structured_v2_v4_nocot

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.

Tentoumaru/lora_structeval_t_unsloth_qwen3_4b_v2_v4_nocot

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

Tentoumaru/
lora_structeval_t_unsloth_qwen3_4b_v2_v4_nocot