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
| 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. | |