dpo
This model is a fine-tuned version of /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
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