--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit library_name: peft license: llama3.1 tags: - trl - sft - unsloth - generated_from_trainer model-index: - name: judicial-summarization-llama-3-finetuned_sci-headnotes_maxData results: [] --- # judicial-summarization-llama-3-finetuned_sci-headnotes_maxData This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co./unsloth/meta-llama-3.1-8b-instruct-bnb-4bit) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6641 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2804 | 1.0 | 726 | 1.3918 | | 1.392 | 2.0 | 1452 | 1.3744 | | 1.1555 | 3.0 | 2178 | 1.3951 | | 0.9796 | 4.0 | 2904 | 1.4558 | | 0.9031 | 5.0 | 3630 | 1.5524 | | 0.7454 | 6.0 | 4356 | 1.6641 | ### Framework versions - PEFT 0.12.0 - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1