llama-7b-dpo-qlora
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.5797
- Rewards/chosen: -0.7180
- Rewards/rejected: -1.2522
- Rewards/accuracies: 0.7163
- Rewards/margins: 0.5342
- Logps/rejected: -439.3930
- Logps/chosen: -418.4136
- Logits/rejected: -0.5278
- Logits/chosen: -0.4875
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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6856 | 0.05 | 100 | 0.6868 | 0.0843 | 0.0692 | 0.5377 | 0.0151 | -307.2546 | -338.1842 | -0.3397 | -0.3142 |
| 0.6704 | 0.1 | 200 | 0.6715 | 0.2423 | 0.1804 | 0.5714 | 0.0619 | -296.1337 | -322.3911 | -0.3758 | -0.3406 |
| 0.6506 | 0.16 | 300 | 0.6529 | 0.1559 | 0.0442 | 0.6647 | 0.1117 | -309.7589 | -331.0275 | -0.4759 | -0.4428 |
| 0.6372 | 0.21 | 400 | 0.6272 | -0.1132 | -0.3130 | 0.6865 | 0.1998 | -345.4769 | -357.9352 | -0.5776 | -0.5492 |
| 0.6233 | 0.26 | 500 | 0.6162 | -0.1577 | -0.4261 | 0.6825 | 0.2685 | -356.7882 | -362.3849 | -0.5820 | -0.5495 |
| 0.5951 | 0.31 | 600 | 0.6063 | -0.3417 | -0.6825 | 0.6806 | 0.3408 | -382.4303 | -380.7912 | -0.6100 | -0.5733 |
| 0.6051 | 0.37 | 700 | 0.5973 | -0.4906 | -0.8807 | 0.6944 | 0.3901 | -402.2431 | -395.6783 | -0.6108 | -0.5761 |
| 0.5632 | 0.42 | 800 | 0.5928 | -0.6334 | -1.0835 | 0.7024 | 0.4501 | -422.5295 | -409.9586 | -0.6245 | -0.5841 |
| 0.6015 | 0.47 | 900 | 0.5896 | -0.6102 | -1.0642 | 0.7123 | 0.4540 | -420.5953 | -407.6412 | -0.5756 | -0.5359 |
| 0.5756 | 0.52 | 1000 | 0.5865 | -0.6474 | -1.1215 | 0.6984 | 0.4742 | -426.3284 | -411.3543 | -0.5431 | -0.5058 |
| 0.6024 | 0.58 | 1100 | 0.5855 | -0.7264 | -1.2283 | 0.7063 | 0.5018 | -437.0025 | -419.2626 | -0.5501 | -0.5104 |
| 0.5578 | 0.63 | 1200 | 0.5823 | -0.6906 | -1.1994 | 0.7143 | 0.5087 | -434.1114 | -415.6815 | -0.5297 | -0.4896 |
| 0.5243 | 0.68 | 1300 | 0.5803 | -0.7453 | -1.2720 | 0.7143 | 0.5267 | -441.3783 | -421.1522 | -0.5340 | -0.4930 |
| 0.5343 | 0.73 | 1400 | 0.5805 | -0.7354 | -1.2662 | 0.7103 | 0.5308 | -440.8000 | -420.1602 | -0.5271 | -0.4872 |
| 0.5707 | 0.79 | 1500 | 0.5799 | -0.7179 | -1.2504 | 0.7123 | 0.5326 | -439.2190 | -418.4040 | -0.5268 | -0.4864 |
| 0.5582 | 0.84 | 1600 | 0.5795 | -0.7300 | -1.2655 | 0.7123 | 0.5355 | -440.7271 | -419.6230 | -0.5271 | -0.4870 |
| 0.5722 | 0.89 | 1700 | 0.5798 | -0.7181 | -1.2517 | 0.7143 | 0.5336 | -439.3442 | -418.4286 | -0.5279 | -0.4876 |
| 0.5964 | 0.94 | 1800 | 0.5796 | -0.7165 | -1.2507 | 0.7163 | 0.5342 | -439.2476 | -418.2664 | -0.5278 | -0.4875 |
| 0.5896 | 0.99 | 1900 | 0.5797 | -0.7180 | -1.2521 | 0.7163 | 0.5341 | -439.3842 | -418.4147 | -0.5278 | -0.4875 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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