metadata
base_model: PKU-Alignment/alpaca-7b-reproduced
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- PKU-Alignment/PKU-SafeRLHF
model-index:
- name: dpo-selective-alpaca
results: []
dpo-selective-alpaca
This model is a fine-tuned version of PKU-Alignment/alpaca-7b-reproduced on the PKU-Alignment/PKU-SafeRLHF dataset. It achieves the following results on the evaluation set:
- Loss: 4659.3857
- Rewards/chosen: -0.2274
- Rewards/rejected: -0.2645
- Rewards/accuracies: 0.6342
- Rewards/margins: 0.0372
- Rewards/safe Rewards: -0.2254
- Rewards/unsafe Rewards: -0.2253
- Logps/rejected: -174.8009
- Logps/chosen: -202.5513
- Logits/rejected: -1.7296
- Logits/chosen: -1.5835
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-07
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Rewards/safe Rewards | Rewards/unsafe Rewards | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4842.2766 | 0.11 | 500 | 4952.8877 | 0.0166 | 0.0096 | 0.6573 | 0.0070 | 0.0166 | 0.0165 | -147.3908 | -178.1579 | -1.7834 | -1.6386 |
| 4764.3852 | 0.22 | 1000 | 4865.9209 | -0.0099 | -0.0282 | 0.6644 | 0.0184 | -0.0094 | -0.0098 | -151.1701 | -180.8021 | -1.7281 | -1.5780 |
| 4814.1586 | 0.32 | 1500 | 4783.4697 | -0.1011 | -0.1298 | 0.6566 | 0.0286 | -0.1003 | -0.1009 | -161.3237 | -189.9300 | -1.7085 | -1.5581 |
| 4693.2395 | 0.43 | 2000 | 4735.1978 | -0.1597 | -0.1926 | 0.6480 | 0.0329 | -0.1583 | -0.1588 | -167.6019 | -195.7835 | -1.7080 | -1.5598 |
| 4747.273 | 0.54 | 2500 | 4701.7651 | -0.1978 | -0.2321 | 0.6416 | 0.0344 | -0.1960 | -0.1962 | -171.5614 | -199.5948 | -1.7166 | -1.5693 |
| 4464.0027 | 0.65 | 3000 | 4681.6167 | -0.2061 | -0.2411 | 0.6356 | 0.0350 | -0.2041 | -0.2043 | -172.4578 | -200.4294 | -1.7240 | -1.5768 |
| 4613.8953 | 0.75 | 3500 | 4667.7300 | -0.2201 | -0.2561 | 0.6333 | 0.0360 | -0.2182 | -0.2182 | -173.9565 | -201.8304 | -1.7289 | -1.5822 |
| 4642.2859 | 0.86 | 4000 | 4661.8745 | -0.2258 | -0.2627 | 0.6336 | 0.0369 | -0.2238 | -0.2238 | -174.6188 | -202.3950 | -1.7298 | -1.5833 |
| 4747.2375 | 0.97 | 4500 | 4659.3687 | -0.2266 | -0.2638 | 0.6363 | 0.0372 | -0.2246 | -0.2245 | -174.7243 | -202.4745 | -1.7302 | -1.5838 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0