metadata
library_name: transformers
license: apache-2.0
base_model: tsavage68/Na_M2_1000steps_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Na_M2_1000steps_1e8rate_03beta_cSFTDPO
results: []
Na_M2_1000steps_1e8rate_03beta_cSFTDPO
This model is a fine-tuned version of tsavage68/Na_M2_1000steps_1e7_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4450
- Rewards/chosen: 0.1680
- Rewards/rejected: -0.4255
- Rewards/accuracies: 1.0
- Rewards/margins: 0.5934
- Logps/rejected: -81.3416
- Logps/chosen: -47.5724
- Logits/rejected: -2.5355
- Logits/chosen: -2.5481
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-08
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6955 | 0.2667 | 50 | 0.6882 | 0.0099 | -0.0031 | 0.5600 | 0.0130 | -79.9338 | -48.0995 | -2.5354 | -2.5481 |
| 0.6761 | 0.5333 | 100 | 0.6730 | 0.0130 | -0.0315 | 0.6600 | 0.0445 | -80.0283 | -48.0889 | -2.5363 | -2.5489 |
| 0.6154 | 0.8 | 150 | 0.5971 | 0.0672 | -0.1393 | 0.9800 | 0.2065 | -80.3878 | -47.9083 | -2.5367 | -2.5493 |
| 0.5735 | 1.0667 | 200 | 0.5430 | 0.1029 | -0.2302 | 1.0 | 0.3331 | -80.6906 | -47.7893 | -2.5352 | -2.5478 |
| 0.5047 | 1.3333 | 250 | 0.5020 | 0.1363 | -0.3030 | 1.0 | 0.4393 | -80.9334 | -47.6779 | -2.5353 | -2.5478 |
| 0.4525 | 1.6 | 300 | 0.4751 | 0.1411 | -0.3685 | 1.0 | 0.5096 | -81.1517 | -47.6622 | -2.5350 | -2.5476 |
| 0.451 | 1.8667 | 350 | 0.4572 | 0.1576 | -0.3988 | 1.0 | 0.5564 | -81.2528 | -47.6072 | -2.5350 | -2.5475 |
| 0.4434 | 2.1333 | 400 | 0.4501 | 0.1391 | -0.4387 | 1.0 | 0.5778 | -81.3857 | -47.6686 | -2.5351 | -2.5477 |
| 0.4313 | 2.4 | 450 | 0.4454 | 0.1528 | -0.4370 | 1.0 | 0.5899 | -81.3802 | -47.6230 | -2.5343 | -2.5469 |
| 0.4546 | 2.6667 | 500 | 0.4513 | 0.1462 | -0.4293 | 1.0 | 0.5755 | -81.3544 | -47.6450 | -2.5345 | -2.5471 |
| 0.4526 | 2.9333 | 550 | 0.4424 | 0.1917 | -0.4110 | 1.0 | 0.6027 | -81.2934 | -47.4934 | -2.5352 | -2.5476 |
| 0.4426 | 3.2 | 600 | 0.4437 | 0.1805 | -0.4175 | 1.0 | 0.5980 | -81.3150 | -47.5307 | -2.5361 | -2.5486 |
| 0.4452 | 3.4667 | 650 | 0.4403 | 0.1651 | -0.4392 | 1.0 | 0.6043 | -81.3875 | -47.5821 | -2.5347 | -2.5473 |
| 0.418 | 3.7333 | 700 | 0.4450 | 0.1668 | -0.4237 | 1.0 | 0.5905 | -81.3358 | -47.5764 | -2.5348 | -2.5474 |
| 0.4281 | 4.0 | 750 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
| 0.4503 | 4.2667 | 800 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
| 0.4372 | 4.5333 | 850 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
| 0.4135 | 4.8 | 900 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
| 0.4316 | 5.0667 | 950 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
| 0.4438 | 5.3333 | 1000 | 0.4450 | 0.1680 | -0.4255 | 1.0 | 0.5934 | -81.3416 | -47.5724 | -2.5355 | -2.5481 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1