Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

llm-compe-wave1-higher-lr-clean – AI Model by a1273352 | AlphaNeural AI

You can deploy this model and start earning money today!

a1273352/
llm-compe-wave1-higher-lr-clean

like

0

structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v2

Qwen/Qwen3-4B-Instruct-2507

Views

No views yet

Wave1-HigherLR-Best-JSON-Clean

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using SFT with LoRA (r=64, alpha=128, lr=2e-6).

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).

Training Configuration

Method: SFT with LoRA (r=64, alpha=128, lr=2e-6)
Base model: Qwen/Qwen3-4B-Instruct-2507
Dataset: u-10bei/structured_data_with_cot_dataset_512_v2

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

Training data: u-10bei/structured_data_with_cot_dataset_512_v2

Dataset License: MIT License. Compliance: Users must comply with the MIT license and the base model's terms.