matsuollm2025_main_lora_repo_v6 – AI Model by 255yossya | AlphaNeural AI

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255yossya/
matsuollm2025_main_lora_repo_v6

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structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v2

u-10bei/structured_data_with_cot_dataset_512_v4

u-10bei/structured_data_with_cot_dataset_512_v5

u-10bei/structured_data_with_cot_dataset_512

u-10bei/structured_data_with_cot_dataset_v2

u-10bei/structured_data_with_cot_dataset

Qwen/Qwen3-4B-Instruct-2507

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qwen3-4b-json-yaml-xml-output-lora

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-05
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 = "255yossya/matsuollm2025_main_lora_repo_v6"

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_v2
u-10bei/structured_data_with_cot_dataset_512_v4
u-10bei/structured_data_with_cot_dataset_512_v5
u-10bei/structured_data_with_cot_dataset_512
u-10bei/structured_data_with_cot_dataset_v2
u-10bei/structured_data_with_cot_dataset

License notes:

u-10bei/* datasets are MIT-licensed.
daichira/* datasets are CC-BY-4.0 licensed (attribution required).
Please comply with all applicable dataset licenses and the base model's terms of use.