Instructions to use kawatoshi3/exp2a-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kawatoshi3/exp2a-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "kawatoshi3/exp2a-lora") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_v2
language:
- en
license: cc-by-4.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
HyperParam Tuning LoRA (max_seq_len=2048)
LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth).
Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Dataset: u-10bei/structured_data_with_cot_dataset_v2
- Method: QLoRA (4-bit)
- Max sequence length: 2048
- Epochs: 3
- Learning rate: 0.0001
- LoRA: r=64, alpha=128
Sources & License
- Training Data: u-10bei/structured_data_with_cot_dataset_v2
- Dataset License: CC-BY-4.0. This dataset is used and can be redistributed under the terms of the CC-BY-4.0 license.
- Compliance: Users must comply with both the dataset attribution requirements and the base model original terms of use.