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m25-qwen3-4b-mixv5-hard4k-l512-lr1e6-r64a128-bs16-e1-s3407

Name: m25-qwen3-4b-mixv5-hard4k-l512-lr1e6-r64a128-bs16-e1-s3407
Availability: InStock

u-10bei/structured_data_with_cot_dataset_512_v5

daichira/structured-hard-sft-4k

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

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Matsuo2025Main_qwen3-4b2507_mix-v5+hard4k_L512_lr1e-6_r64a128_bs16_ep1_s3407

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.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
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 = "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)

Sources & Terms (IMPORTANT)

Training data:

u-10bei/structured_data_with_cot_dataset_512_v5
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.

RegFo/m25-qwen3-4b-mixv5-hard4k-l512-lr1e6-r64a128-bs16-e1-s3407