qwen_cfUNL_entropy
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: 0.0000
- Rewards/chosen: -45.0891
- Rewards/rejected: -46.1094
- Rewards/accuracies: 0.5682
- Rewards/margins: 1.0204
- Logps/rejected: -46.1094
- Logps/chosen: -45.0891
- Logits/rejected: 7.4245
- Logits/chosen: 7.7499
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 | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.0 | 0.2141 | 400 | 0.0001 | -30.9341 | -32.7194 | 0.5697 | 1.7853 | -32.7194 | -30.9341 | 4.5291 | 4.5264 |
0.0 | 0.4282 | 800 | 0.0000 | -38.7534 | -40.2065 | 0.5593 | 1.4531 | -40.2065 | -38.7534 | 6.2341 | 6.3877 |
0.0009 | 0.6422 | 1200 | 0.0000 | -38.5460 | -39.9578 | 0.5512 | 1.4119 | -39.9578 | -38.5460 | 6.1244 | 6.2779 |
0.0 | 0.8563 | 1600 | 0.0000 | -40.0222 | -41.4115 | 0.5690 | 1.3893 | -41.4115 | -40.0222 | 6.5494 | 6.7346 |
0.0 | 1.0704 | 2000 | 0.0000 | -43.0566 | -44.2275 | 0.5653 | 1.1709 | -44.2275 | -43.0566 | 7.0818 | 7.3504 |
0.0 | 1.2845 | 2400 | 0.0000 | -43.5288 | -44.6477 | 0.5645 | 1.1189 | -44.6477 | -43.5288 | 7.1882 | 7.4775 |
0.0 | 1.4986 | 2800 | 0.0000 | -43.7383 | -44.8584 | 0.5660 | 1.1201 | -44.8584 | -43.7383 | 7.1745 | 7.4634 |
0.0 | 1.7127 | 3200 | 0.0000 | -44.4950 | -45.5556 | 0.5638 | 1.0605 | -45.5556 | -44.4950 | 7.2848 | 7.5950 |
0.0 | 1.9267 | 3600 | 0.0000 | -44.5958 | -45.6569 | 0.5645 | 1.0611 | -45.6569 | -44.5958 | 7.2814 | 7.5948 |
0.0 | 2.1408 | 4000 | 0.0000 | -44.8271 | -45.8411 | 0.5668 | 1.0140 | -45.8411 | -44.8271 | 7.4235 | 7.7436 |
0.0 | 2.3549 | 4400 | 0.0000 | -45.1344 | -46.1374 | 0.5653 | 1.0030 | -46.1374 | -45.1344 | 7.3526 | 7.6831 |
0.0 | 2.5690 | 4800 | 0.0000 | -45.0201 | -46.0501 | 0.5653 | 1.0300 | -46.0501 | -45.0201 | 7.3843 | 7.7103 |
0.0 | 2.7831 | 5200 | 0.0000 | -45.3432 | -46.3394 | 0.5653 | 0.9961 | -46.3394 | -45.3432 | 7.4499 | 7.7830 |
0.0 | 2.9972 | 5600 | 0.0000 | -45.0891 | -46.1094 | 0.5682 | 1.0204 | -46.1094 | -45.0891 | 7.4245 | 7.7499 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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