Instructions to use codeaidbackUp/CouplingSmells-Detection-Adpater with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use codeaidbackUp/CouplingSmells-Detection-Adpater with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "codeaidbackUp/CouplingSmells-Detection-Adpater") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen2.5-14B-Instruct | |
| tags: | |
| - llama-factory | |
| - lora | |
| - generated_from_trainer | |
| model-index: | |
| - name: coupling_model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # coupling_model | |
| This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the couplingDetection_finetune_train dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4664 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0001 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 8 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 0.656 | 0.1736 | 100 | 0.5767 | | |
| | 0.5695 | 0.3472 | 200 | 0.5406 | | |
| | 0.5849 | 0.5208 | 300 | 0.4985 | | |
| | 0.527 | 0.6944 | 400 | 0.4857 | | |
| | 0.4782 | 0.8681 | 500 | 0.4704 | | |
| | 0.4357 | 1.0417 | 600 | 0.4712 | | |
| | 0.4033 | 1.2153 | 700 | 0.4654 | | |
| | 0.4438 | 1.3889 | 800 | 0.4594 | | |
| | 0.4297 | 1.5625 | 900 | 0.4532 | | |
| | 0.4012 | 1.7361 | 1000 | 0.4480 | | |
| | 0.2866 | 1.9097 | 1100 | 0.4806 | | |
| | 0.2718 | 2.0833 | 1200 | 0.4796 | | |
| | 0.3169 | 2.2569 | 1300 | 0.4686 | | |
| | 0.276 | 2.4306 | 1400 | 0.4692 | | |
| | 0.2616 | 2.6042 | 1500 | 0.4693 | | |
| | 0.2545 | 2.7778 | 1600 | 0.4671 | | |
| | 0.2673 | 2.9514 | 1700 | 0.4665 | | |
| ### Framework versions | |
| - PEFT 0.15.2 | |
| - Transformers 4.52.4 | |
| - Pytorch 2.7.0+cu128 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 |