metadata
base_model: princeton-nlp/Llama-3-Base-8B-SFT
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: llama-8b-dpo-full
results: []
llama-8b-dpo-full
This model is a fine-tuned version of princeton-nlp/Llama-3-Base-8B-SFT on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.6316
- Rewards/chosen: 0.6899
- Rewards/rejected: 0.3044
- Rewards/accuracies: 0.6600
- Rewards/margins: 0.3855
- Logps/rejected: -2200.0752
- Logps/chosen: -2603.7832
- Logits/rejected: -1.4288
- Logits/chosen: -1.4752
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_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 | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6558 | 0.05 | 100 | 0.6527 | 0.7712 | 0.5799 | 0.5740 | 0.1913 | -2172.5291 | -2595.6543 | -1.1822 | -1.2241 |
| 0.6404 | 0.1 | 200 | 0.6911 | 0.4590 | 0.2677 | 0.5860 | 0.1913 | -2203.7483 | -2626.8760 | -1.2019 | -1.2423 |
| 0.6725 | 0.16 | 300 | 0.6603 | 0.8108 | 0.5231 | 0.6320 | 0.2877 | -2178.2058 | -2591.6921 | -1.3149 | -1.3646 |
| 0.689 | 0.21 | 400 | 0.6529 | 0.8101 | 0.4993 | 0.6280 | 0.3108 | -2180.5830 | -2591.7649 | -1.4428 | -1.5029 |
| 0.6682 | 0.26 | 500 | 0.6674 | 0.9667 | 0.6125 | 0.6420 | 0.3542 | -2169.2654 | -2576.1008 | -1.5148 | -1.5665 |
| 0.6309 | 0.31 | 600 | 0.6445 | 0.8348 | 0.4673 | 0.6580 | 0.3675 | -2183.7852 | -2589.2971 | -1.5885 | -1.6449 |
| 0.6467 | 0.37 | 700 | 0.6482 | 0.8852 | 0.5455 | 0.6240 | 0.3397 | -2175.9651 | -2584.2512 | -1.6562 | -1.7105 |
| 0.6215 | 0.42 | 800 | 0.6453 | 1.0902 | 0.6825 | 0.6380 | 0.4077 | -2162.2678 | -2563.7546 | -1.6541 | -1.7085 |
| 0.6674 | 0.47 | 900 | 0.6416 | 0.7802 | 0.4490 | 0.6440 | 0.3312 | -2185.6135 | -2594.7568 | -1.5145 | -1.5652 |
| 0.644 | 0.52 | 1000 | 0.6500 | 0.7077 | 0.3679 | 0.6400 | 0.3398 | -2193.7285 | -2602.0039 | -1.4506 | -1.5047 |
| 0.6539 | 0.58 | 1100 | 0.6389 | 0.8477 | 0.4852 | 0.6500 | 0.3625 | -2181.9937 | -2588.0068 | -1.4697 | -1.5227 |
| 0.7267 | 0.63 | 1200 | 0.6421 | 0.5390 | 0.2257 | 0.6620 | 0.3133 | -2207.9438 | -2618.8738 | -1.6292 | -1.6800 |
| 0.5746 | 0.68 | 1300 | 0.6301 | 0.9057 | 0.4892 | 0.6660 | 0.4164 | -2181.5920 | -2582.2095 | -1.4994 | -1.5461 |
| 0.6053 | 0.73 | 1400 | 0.6342 | 0.8758 | 0.4563 | 0.6660 | 0.4196 | -2184.8909 | -2585.1914 | -1.4440 | -1.4891 |
| 0.6232 | 0.79 | 1500 | 0.6324 | 0.8055 | 0.3994 | 0.6580 | 0.4062 | -2190.5796 | -2592.2219 | -1.4283 | -1.4759 |
| 0.6326 | 0.84 | 1600 | 0.6392 | 0.4525 | 0.1032 | 0.6560 | 0.3493 | -2220.1997 | -2627.5283 | -1.4501 | -1.4959 |
| 0.6469 | 0.89 | 1700 | 0.6306 | 0.7453 | 0.3498 | 0.6660 | 0.3955 | -2195.5359 | -2598.2412 | -1.4289 | -1.4758 |
| 0.669 | 0.94 | 1800 | 0.6323 | 0.6544 | 0.2748 | 0.6600 | 0.3796 | -2203.0393 | -2607.3367 | -1.4308 | -1.4769 |
| 0.6531 | 0.99 | 1900 | 0.6317 | 0.6900 | 0.3040 | 0.6640 | 0.3860 | -2200.1182 | -2603.7776 | -1.4289 | -1.4754 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2