exp_camelcase

Model ID: ekunish/exp_camelcase

exp008a + camelCase augmented data (21K + 1.6K camelCase conversion variants)

Training Configuration

Parameter	Value
Base model	Qwen/Qwen3-4B-Instruct-2507
Method	QLoRA (4-bit)
Max sequence length	512
Epochs	1
Learning rate	1e-06
LoRA r	64
LoRA alpha	128
Batch size	2 × 8 = 16

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "ekunish/exp_camelcase"

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

Training Data

• Dataset: data/sft_u10bei_camelcase
• License: CC-BY-4.0 (where applicable)

Sources & License

• Training Data: u-10bei/structured_data_with_cot_dataset_512_v2, daichira/structured-3k-mix-sft, etc.
• Dataset License: Creative Commons Attribution (CC-BY-4.0)
• Compliance: Users must comply with both the dataset's attribution requirements and the base model's original terms of use.

Competition

松尾研LLMコミュニティ 2025年度講座 メインコンペ (StructEval-T)