--- library_name: peft tags: - alignment-handbook - generated_from_trainer base_model: g8a9/tweety-mistral-7b datasets: - giux78/ultrafeedback-binarized-preferences-cleaned-ita model-index: - name: dpo results: [] --- # dpo This model is a fine-tuned version of [/leonardo_scratch/fast/IscrC_ItaLLM_0/tweety_models/sft](https://huggingface.co//leonardo_scratch/fast/IscrC_ItaLLM_0/tweety_models/sft) on the giux78/ultrafeedback-binarized-preferences-cleaned-ita dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Rewards/chosen: -0.0430 - Rewards/rejected: -0.0430 - Rewards/accuracies: 0.0 - Rewards/margins: 0.0 - Logps/rejected: -310.7832 - Logps/chosen: -310.7832 - Logits/rejected: -2.3909 - Logits/chosen: -2.3909 ## 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-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - 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: 1 ### Training results | Training Loss | Epoch | Step | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected | |:-------------:|:------:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:------------------:|:--------------:|:---------------:|:----------------:| | 0.6931 | 0.0292 | 100 | -2.3941 | -2.3941 | -306.3899 | -306.3899 | 0.6931 | 0.0 | 0.0009 | 0.0 | 0.0009 | | 0.6931 | 0.0584 | 200 | -2.3946 | -2.3946 | -306.5539 | -306.5539 | 0.6931 | 0.0 | -0.0008 | 0.0 | -0.0008 | | 0.6931 | 0.0876 | 300 | -2.3942 | -2.3942 | -307.0490 | -307.0490 | 0.6931 | 0.0 | -0.0057 | 0.0 | -0.0057 | | 0.6931 | 0.1168 | 400 | -2.3940 | -2.3940 | -307.3796 | -307.3796 | 0.6931 | 0.0 | -0.0090 | 0.0 | -0.0090 | | 0.6931 | 0.1460 | 500 | -2.3937 | -2.3937 | -307.1581 | -307.1581 | 0.6931 | 0.0 | -0.0068 | 0.0 | -0.0068 | | 0.6931 | 0.1751 | 600 | -2.3950 | -2.3950 | -306.9631 | -306.9631 | 0.6931 | 0.0 | -0.0048 | 0.0 | -0.0048 | | 0.6931 | 0.2043 | 700 | -2.3949 | -2.3949 | -307.6349 | -307.6349 | 0.6931 | 0.0 | -0.0116 | 0.0 | -0.0116 | | 0.6931 | 0.2335 | 800 | -2.3947 | -2.3947 | -307.6957 | -307.6957 | 0.6931 | 0.0 | -0.0122 | 0.0 | -0.0122 | | 0.6931 | 0.2627 | 900 | -2.3968 | -2.3968 | -307.1708 | -307.1708 | 0.6931 | 0.0 | -0.0069 | 0.0 | -0.0069 | | 0.6931 | 0.2919 | 1000 | -2.3967 | -2.3967 | -308.2130 | -308.2130 | 0.6931 | 0.0 | -0.0173 | 0.0 | -0.0173 | | 0.6931 | 0.3211 | 1100 | -2.3971 | -2.3971 | -309.4724 | -309.4724 | 0.6931 | 0.0 | -0.0299 | 0.0 | -0.0299 | | 0.6931 | 0.3503 | 1200 | -2.3976 | -2.3976 | -310.0194 | -310.0194 | 0.6931 | 0.0 | -0.0354 | 0.0 | -0.0354 | | 0.6931 | 0.3795 | 1300 | -2.3963 | -2.3963 | -309.5114 | -309.5114 | 0.6931 | 0.0 | -0.0303 | 0.0 | -0.0303 | | 0.6931 | 0.4087 | 1400 | -2.3955 | -2.3955 | -309.2061 | -309.2061 | 0.6931 | 0.0 | -0.0273 | 0.0 | -0.0273 | | 0.6931 | 0.4379 | 1500 | -2.3943 | -2.3943 | -308.9652 | -308.9652 | 0.6931 | 0.0 | -0.0249 | 0.0 | -0.0249 | | 0.6931 | 0.4671 | 1600 | -2.3954 | -2.3954 | -309.1586 | -309.1586 | 0.6931 | 0.0 | -0.0268 | 0.0 | -0.0268 | | 0.6931 | 0.4962 | 1700 | -2.3913 | -2.3913 | -309.4055 | -309.4055 | 0.6931 | 0.0 | -0.0293 | 0.0 | -0.0293 | | 0.6931 | 0.5254 | 1800 | -2.3927 | -2.3927 | -310.2643 | -310.2643 | 0.6931 | 0.0 | -0.0379 | 0.0 | -0.0379 | | 0.6931 | 0.5546 | 1900 | -2.3927 | -2.3927 | -310.4164 | -310.4164 | 0.6931 | 0.0 | -0.0394 | 0.0 | -0.0394 | | 0.6931 | 0.5838 | 2000 | -2.3920 | -2.3920 | -310.4427 | -310.4427 | 0.6931 | 0.0 | -0.0396 | 0.0 | -0.0396 | | 0.6931 | 0.6130 | 2100 | -2.3901 | -2.3901 | -310.7150 | -310.7150 | 0.6931 | 0.0 | -0.0424 | 0.0 | -0.0424 | | 0.6931 | 0.6422 | 2200 | -2.3911 | -2.3911 | -311.0310 | -311.0310 | 0.6931 | 0.0 | -0.0455 | 0.0 | -0.0455 | | 0.6931 | 0.6714 | 2300 | -2.3912 | -2.3912 | -310.7881 | -310.7881 | 0.6931 | 0.0 | -0.0431 | 0.0 | -0.0431 | | 0.6931 | 0.7006 | 2400 | -2.3899 | -2.3899 | -310.6455 | -310.6455 | 0.6931 | 0.0 | -0.0417 | 0.0 | -0.0417 | | 0.6931 | 0.7298 | 2500 | -2.3915 | -2.3915 | -310.8196 | -310.8196 | 0.6931 | 0.0 | -0.0434 | 0.0 | -0.0434 | | 0.6931 | 0.7590 | 2600 | 0.6931 | -0.0438 | -0.0438 | 0.0 | 0.0 | -310.8546 | -310.8546 | -2.3919 | -2.3919 | | 0.6931 | 0.7881 | 2700 | 0.6931 | -0.0436 | -0.0436 | 0.0 | 0.0 | -310.8407 | -310.8407 | -2.3916 | -2.3916 | | 0.6931 | 0.8173 | 2800 | 0.6931 | -0.0432 | -0.0432 | 0.0 | 0.0 | -310.7981 | -310.7981 | -2.3915 | -2.3915 | | 0.6931 | 0.8465 | 2900 | 0.6931 | -0.0432 | -0.0432 | 0.0 | 0.0 | -310.7943 | -310.7943 | -2.3920 | -2.3920 | | 0.6931 | 0.8757 | 3000 | 0.6931 | -0.0431 | -0.0431 | 0.0 | 0.0 | -310.7866 | -310.7866 | -2.3918 | -2.3918 | | 0.6931 | 0.9049 | 3100 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7794 | -310.7794 | -2.3908 | -2.3908 | | 0.6931 | 0.9341 | 3200 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7812 | -310.7812 | -2.3911 | -2.3911 | | 0.6931 | 0.9633 | 3300 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7767 | -310.7767 | -2.3915 | -2.3915 | | 0.6931 | 0.9925 | 3400 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7832 | -310.7832 | -2.3909 | -2.3909 | ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1