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_05beta_cSFTDPO
results: []
Na_M2_1000steps_1e8rate_05beta_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: 0.3285
- Rewards/chosen: 0.2983
- Rewards/rejected: -0.6864
- Rewards/accuracies: 1.0
- Rewards/margins: 0.9847
- Logps/rejected: -81.2962
- Logps/chosen: -47.5358
- Logits/rejected: -2.5349
- Logits/chosen: -2.5474
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6915 | 0.2667 | 50 | 0.6994 | 0.0060 | 0.0118 | 0.5400 | -0.0058 | -79.8998 | -48.1204 | -2.5353 | -2.5479 |
| 0.6635 | 0.5333 | 100 | 0.6459 | 0.0371 | -0.0697 | 0.7100 | 0.1068 | -80.0629 | -48.0583 | -2.5354 | -2.5480 |
| 0.5585 | 0.8 | 150 | 0.5484 | 0.1041 | -0.2242 | 0.9400 | 0.3283 | -80.3718 | -47.9241 | -2.5344 | -2.5470 |
| 0.5041 | 1.0667 | 200 | 0.4568 | 0.1548 | -0.4106 | 1.0 | 0.5654 | -80.7446 | -47.8228 | -2.5349 | -2.5475 |
| 0.4012 | 1.3333 | 250 | 0.3983 | 0.2253 | -0.5152 | 1.0 | 0.7405 | -80.9538 | -47.6818 | -2.5354 | -2.5479 |
| 0.3304 | 1.6 | 300 | 0.3692 | 0.2306 | -0.6109 | 1.0 | 0.8415 | -81.1452 | -47.6712 | -2.5346 | -2.5472 |
| 0.3396 | 1.8667 | 350 | 0.3524 | 0.2373 | -0.6582 | 1.0 | 0.8955 | -81.2397 | -47.6578 | -2.5349 | -2.5474 |
| 0.3311 | 2.1333 | 400 | 0.3304 | 0.2656 | -0.7177 | 1.0 | 0.9834 | -81.3589 | -47.6011 | -2.5350 | -2.5475 |
| 0.3099 | 2.4 | 450 | 0.3378 | 0.2807 | -0.6665 | 1.0 | 0.9472 | -81.2563 | -47.5710 | -2.5361 | -2.5486 |
| 0.3384 | 2.6667 | 500 | 0.3271 | 0.2743 | -0.7151 | 1.0 | 0.9894 | -81.3535 | -47.5838 | -2.5349 | -2.5474 |
| 0.3381 | 2.9333 | 550 | 0.3284 | 0.2854 | -0.7005 | 1.0 | 0.9859 | -81.3243 | -47.5616 | -2.5347 | -2.5472 |
| 0.3328 | 3.2 | 600 | 0.3217 | 0.2963 | -0.7183 | 1.0 | 1.0146 | -81.3600 | -47.5398 | -2.5349 | -2.5474 |
| 0.3162 | 3.4667 | 650 | 0.3252 | 0.3046 | -0.6916 | 1.0 | 0.9962 | -81.3066 | -47.5232 | -2.5358 | -2.5483 |
| 0.2907 | 3.7333 | 700 | 0.3331 | 0.3002 | -0.6711 | 1.0 | 0.9713 | -81.2656 | -47.5319 | -2.5350 | -2.5475 |
| 0.3052 | 4.0 | 750 | 0.3279 | 0.2998 | -0.6877 | 1.0 | 0.9875 | -81.2988 | -47.5328 | -2.5350 | -2.5474 |
| 0.3264 | 4.2667 | 800 | 0.3285 | 0.2983 | -0.6864 | 1.0 | 0.9847 | -81.2962 | -47.5358 | -2.5349 | -2.5474 |
| 0.3196 | 4.5333 | 850 | 0.3285 | 0.2983 | -0.6864 | 1.0 | 0.9847 | -81.2962 | -47.5358 | -2.5349 | -2.5474 |
| 0.2962 | 4.8 | 900 | 0.3285 | 0.2983 | -0.6864 | 1.0 | 0.9847 | -81.2962 | -47.5358 | -2.5349 | -2.5474 |
| 0.3115 | 5.0667 | 950 | 0.3285 | 0.2983 | -0.6864 | 1.0 | 0.9847 | -81.2962 | -47.5358 | -2.5349 | -2.5474 |
| 0.3285 | 5.3333 | 1000 | 0.3285 | 0.2983 | -0.6864 | 1.0 | 0.9847 | -81.2962 | -47.5358 | -2.5349 | -2.5474 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1