--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct datasets: - GaetanMichelet/chat-60_ft_task-1_auto - GaetanMichelet/chat-120_ft_task-1_auto library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: Llama-31-8B_task-1_120-samples_config-2_full_auto results: [] --- # Llama-31-8B_task-1_120-samples_config-2_full_auto This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) on the GaetanMichelet/chat-60_ft_task-1_auto and the GaetanMichelet/chat-120_ft_task-1_auto datasets. It achieves the following results on the evaluation set: - Loss: 0.7873 ## 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 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 2.1572 | 0.9091 | 5 | 2.1149 | | 2.0048 | 2.0 | 11 | 1.8150 | | 1.651 | 2.9091 | 16 | 1.5524 | | 1.2397 | 4.0 | 22 | 1.1141 | | 0.9602 | 4.9091 | 27 | 0.9207 | | 0.8646 | 6.0 | 33 | 0.8729 | | 0.7895 | 6.9091 | 38 | 0.8490 | | 0.7762 | 8.0 | 44 | 0.8273 | | 0.7412 | 8.9091 | 49 | 0.8120 | | 0.6669 | 10.0 | 55 | 0.7971 | | 0.6184 | 10.9091 | 60 | 0.7873 | | 0.5857 | 12.0 | 66 | 0.7943 | | 0.5374 | 12.9091 | 71 | 0.8102 | | 0.4629 | 14.0 | 77 | 0.8345 | | 0.396 | 14.9091 | 82 | 0.8902 | | 0.336 | 16.0 | 88 | 0.9196 | | 0.2438 | 16.9091 | 93 | 1.0492 | | 0.1943 | 18.0 | 99 | 1.1073 | ### Framework versions - PEFT 0.12.0 - Transformers 4.44.0 - Pytorch 2.1.2+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1