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--- |
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library_name: peft |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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base_model: g8a9/tweety-mistral-7b |
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datasets: |
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- giux78/ultrafeedback-binarized-preferences-cleaned-ita |
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model-index: |
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- name: dpo |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# dpo |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6931 |
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- Rewards/chosen: -0.0430 |
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- Rewards/rejected: -0.0430 |
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- Rewards/accuracies: 0.0 |
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- Rewards/margins: 0.0 |
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- Logps/rejected: -310.7832 |
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- Logps/chosen: -310.7832 |
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- Logits/rejected: -2.3909 |
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- Logits/chosen: -2.3909 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected | |
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|:-------------:|:------:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:------------------:|:--------------:|:---------------:|:----------------:| |
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| 0.6931 | 0.0292 | 100 | -2.3941 | -2.3941 | -306.3899 | -306.3899 | 0.6931 | 0.0 | 0.0009 | 0.0 | 0.0009 | |
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| 0.6931 | 0.0584 | 200 | -2.3946 | -2.3946 | -306.5539 | -306.5539 | 0.6931 | 0.0 | -0.0008 | 0.0 | -0.0008 | |
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| 0.6931 | 0.0876 | 300 | -2.3942 | -2.3942 | -307.0490 | -307.0490 | 0.6931 | 0.0 | -0.0057 | 0.0 | -0.0057 | |
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| 0.6931 | 0.1168 | 400 | -2.3940 | -2.3940 | -307.3796 | -307.3796 | 0.6931 | 0.0 | -0.0090 | 0.0 | -0.0090 | |
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| 0.6931 | 0.1460 | 500 | -2.3937 | -2.3937 | -307.1581 | -307.1581 | 0.6931 | 0.0 | -0.0068 | 0.0 | -0.0068 | |
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| 0.6931 | 0.1751 | 600 | -2.3950 | -2.3950 | -306.9631 | -306.9631 | 0.6931 | 0.0 | -0.0048 | 0.0 | -0.0048 | |
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| 0.6931 | 0.2043 | 700 | -2.3949 | -2.3949 | -307.6349 | -307.6349 | 0.6931 | 0.0 | -0.0116 | 0.0 | -0.0116 | |
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| 0.6931 | 0.2335 | 800 | -2.3947 | -2.3947 | -307.6957 | -307.6957 | 0.6931 | 0.0 | -0.0122 | 0.0 | -0.0122 | |
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| 0.6931 | 0.2627 | 900 | -2.3968 | -2.3968 | -307.1708 | -307.1708 | 0.6931 | 0.0 | -0.0069 | 0.0 | -0.0069 | |
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| 0.6931 | 0.2919 | 1000 | -2.3967 | -2.3967 | -308.2130 | -308.2130 | 0.6931 | 0.0 | -0.0173 | 0.0 | -0.0173 | |
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| 0.6931 | 0.3211 | 1100 | -2.3971 | -2.3971 | -309.4724 | -309.4724 | 0.6931 | 0.0 | -0.0299 | 0.0 | -0.0299 | |
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| 0.6931 | 0.3503 | 1200 | -2.3976 | -2.3976 | -310.0194 | -310.0194 | 0.6931 | 0.0 | -0.0354 | 0.0 | -0.0354 | |
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| 0.6931 | 0.3795 | 1300 | -2.3963 | -2.3963 | -309.5114 | -309.5114 | 0.6931 | 0.0 | -0.0303 | 0.0 | -0.0303 | |
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| 0.6931 | 0.4087 | 1400 | -2.3955 | -2.3955 | -309.2061 | -309.2061 | 0.6931 | 0.0 | -0.0273 | 0.0 | -0.0273 | |
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| 0.6931 | 0.4379 | 1500 | -2.3943 | -2.3943 | -308.9652 | -308.9652 | 0.6931 | 0.0 | -0.0249 | 0.0 | -0.0249 | |
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| 0.6931 | 0.4671 | 1600 | -2.3954 | -2.3954 | -309.1586 | -309.1586 | 0.6931 | 0.0 | -0.0268 | 0.0 | -0.0268 | |
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| 0.6931 | 0.4962 | 1700 | -2.3913 | -2.3913 | -309.4055 | -309.4055 | 0.6931 | 0.0 | -0.0293 | 0.0 | -0.0293 | |
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| 0.6931 | 0.5254 | 1800 | -2.3927 | -2.3927 | -310.2643 | -310.2643 | 0.6931 | 0.0 | -0.0379 | 0.0 | -0.0379 | |
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| 0.6931 | 0.5546 | 1900 | -2.3927 | -2.3927 | -310.4164 | -310.4164 | 0.6931 | 0.0 | -0.0394 | 0.0 | -0.0394 | |
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| 0.6931 | 0.5838 | 2000 | -2.3920 | -2.3920 | -310.4427 | -310.4427 | 0.6931 | 0.0 | -0.0396 | 0.0 | -0.0396 | |
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| 0.6931 | 0.6130 | 2100 | -2.3901 | -2.3901 | -310.7150 | -310.7150 | 0.6931 | 0.0 | -0.0424 | 0.0 | -0.0424 | |
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| 0.6931 | 0.6422 | 2200 | -2.3911 | -2.3911 | -311.0310 | -311.0310 | 0.6931 | 0.0 | -0.0455 | 0.0 | -0.0455 | |
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| 0.6931 | 0.6714 | 2300 | -2.3912 | -2.3912 | -310.7881 | -310.7881 | 0.6931 | 0.0 | -0.0431 | 0.0 | -0.0431 | |
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| 0.6931 | 0.7006 | 2400 | -2.3899 | -2.3899 | -310.6455 | -310.6455 | 0.6931 | 0.0 | -0.0417 | 0.0 | -0.0417 | |
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| 0.6931 | 0.7298 | 2500 | -2.3915 | -2.3915 | -310.8196 | -310.8196 | 0.6931 | 0.0 | -0.0434 | 0.0 | -0.0434 | |
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| 0.6931 | 0.7590 | 2600 | 0.6931 | -0.0438 | -0.0438 | 0.0 | 0.0 | -310.8546 | -310.8546 | -2.3919 | -2.3919 | |
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| 0.6931 | 0.7881 | 2700 | 0.6931 | -0.0436 | -0.0436 | 0.0 | 0.0 | -310.8407 | -310.8407 | -2.3916 | -2.3916 | |
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| 0.6931 | 0.8173 | 2800 | 0.6931 | -0.0432 | -0.0432 | 0.0 | 0.0 | -310.7981 | -310.7981 | -2.3915 | -2.3915 | |
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| 0.6931 | 0.8465 | 2900 | 0.6931 | -0.0432 | -0.0432 | 0.0 | 0.0 | -310.7943 | -310.7943 | -2.3920 | -2.3920 | |
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| 0.6931 | 0.8757 | 3000 | 0.6931 | -0.0431 | -0.0431 | 0.0 | 0.0 | -310.7866 | -310.7866 | -2.3918 | -2.3918 | |
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| 0.6931 | 0.9049 | 3100 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7794 | -310.7794 | -2.3908 | -2.3908 | |
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| 0.6931 | 0.9341 | 3200 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7812 | -310.7812 | -2.3911 | -2.3911 | |
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| 0.6931 | 0.9633 | 3300 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7767 | -310.7767 | -2.3915 | -2.3915 | |
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| 0.6931 | 0.9925 | 3400 | 0.6931 | -0.0430 | -0.0430 | 0.0 | 0.0 | -310.7832 | -310.7832 | -2.3909 | -2.3909 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.40.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
