qwen_cpo_entropy_0_01
This model is a fine-tuned version of trl-lib/qwen1.5-0.5b-sft on the yakazimir/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.5583
- Sft Loss: 3.4705
- Rewards/chosen: -3.3285
- Rewards/rejected: -4.3810
- Rewards/accuracies: 0.7226
- Rewards/margins: 1.0525
- Logps/rejected: -4.3810
- Logps/chosen: -3.3285
- Logits/rejected: 0.2811
- Logits/chosen: 0.1563
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: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_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: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss | Sft Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.7019 | 0.2141 | 400 | 0.6977 | 1.4219 | -1.4375 | -1.6032 | 0.5631 | 0.1657 | -1.6032 | -1.4375 | 0.2993 | 0.2138 |
0.6225 | 0.4282 | 800 | 0.6192 | 2.0573 | -2.0770 | -2.5396 | 0.6669 | 0.4626 | -2.5396 | -2.0770 | 0.3429 | 0.2570 |
0.6242 | 0.6422 | 1200 | 0.5882 | 2.6279 | -2.4850 | -3.1039 | 0.6973 | 0.6190 | -3.1039 | -2.4850 | 0.5237 | 0.4102 |
0.5405 | 0.8563 | 1600 | 0.5781 | 2.5442 | -2.4160 | -3.0202 | 0.7092 | 0.6042 | -3.0202 | -2.4160 | 0.4122 | 0.3042 |
0.6195 | 1.0704 | 2000 | 0.5673 | 2.7121 | -2.5451 | -3.2527 | 0.7129 | 0.7076 | -3.2527 | -2.5451 | 0.4573 | 0.3371 |
0.5895 | 1.2845 | 2400 | 0.5590 | 3.0631 | -2.8962 | -3.7486 | 0.7322 | 0.8524 | -3.7486 | -2.8962 | 0.3362 | 0.2174 |
0.5512 | 1.4986 | 2800 | 0.5563 | 2.9053 | -2.7513 | -3.5751 | 0.7203 | 0.8238 | -3.5751 | -2.7513 | 0.2892 | 0.1750 |
0.5766 | 1.7127 | 3200 | 0.5520 | 2.9643 | -2.8134 | -3.6655 | 0.7263 | 0.8522 | -3.6655 | -2.8134 | 0.2677 | 0.1562 |
0.5625 | 1.9267 | 3600 | 0.5478 | 3.0563 | -2.8597 | -3.7385 | 0.7255 | 0.8788 | -3.7385 | -2.8597 | 0.3670 | 0.2441 |
0.4702 | 2.1408 | 4000 | 0.5592 | 3.5119 | -3.3071 | -4.3285 | 0.7240 | 1.0214 | -4.3285 | -3.3071 | 0.2395 | 0.1198 |
0.4882 | 2.3549 | 4400 | 0.5601 | 3.5201 | -3.3795 | -4.4355 | 0.7270 | 1.0560 | -4.4355 | -3.3795 | 0.2852 | 0.1603 |
0.4952 | 2.5690 | 4800 | 0.5580 | 3.4402 | -3.3065 | -4.3570 | 0.7233 | 1.0505 | -4.3570 | -3.3065 | 0.3210 | 0.1936 |
0.4272 | 2.7831 | 5200 | 0.5579 | 3.4523 | -3.3138 | -4.3619 | 0.7233 | 1.0481 | -4.3619 | -3.3138 | 0.3592 | 0.2281 |
0.459 | 2.9972 | 5600 | 0.5583 | 3.4705 | -3.3285 | -4.3810 | 0.7226 | 1.0525 | -4.3810 | -3.3285 | 0.2811 | 0.1563 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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