--- library_name: peft tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: NbAiLab/nb-gpt-j-6B-v2 datasets: - hugodk-sch/aftonposten_title_prefs model-index: - name: aftonposten-6b-align-scan results: [] --- # aftonposten-6b-align-scan This model is a fine-tuned version of [data/ap-gpt-j-6b-sft-qlora-04-08](https://huggingface.co/data/ap-gpt-j-6b-sft-qlora-04-08) on the hugodk-sch/aftonposten_title_prefs dataset. It achieves the following results on the evaluation set: - Loss: 0.6984 - Rewards/chosen: 0.0026 - Rewards/rejected: 0.0097 - Rewards/accuracies: 0.4988 - Rewards/margins: -0.0071 - Logps/rejected: -37.5004 - Logps/chosen: -34.0302 - Logits/rejected: -2.2394 - Logits/chosen: -2.2443 ## 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: 5e-07 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6933 | 0.26 | 100 | 0.6912 | 0.0148 | 0.0075 | 0.4988 | 0.0073 | -37.5041 | -34.0098 | -2.2391 | -2.2439 | | 0.6827 | 0.52 | 200 | 0.6955 | 0.0061 | 0.0071 | 0.4813 | -0.0010 | -37.5048 | -34.0244 | -2.2385 | -2.2434 | | 0.6911 | 0.78 | 300 | 0.6925 | 0.0061 | 0.0005 | 0.5457 | 0.0056 | -37.5157 | -34.0243 | -2.2394 | -2.2443 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1