metadata
library_name: transformers
license: apache-2.0
base_model: tsavage68/IE_M2_1000steps_1e7rate_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: IE_M2_1000steps_1e7rate_01beta_cSFTDPO
results: []
IE_M2_1000steps_1e7rate_01beta_cSFTDPO
This model is a fine-tuned version of tsavage68/IE_M2_1000steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3743
- Rewards/chosen: -0.3291
- Rewards/rejected: -6.1017
- Rewards/accuracies: 0.4600
- Rewards/margins: 5.7727
- Logps/rejected: -102.0393
- Logps/chosen: -45.4965
- Logits/rejected: -2.8684
- Logits/chosen: -2.8050
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-07
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.558 | 0.4 | 50 | 0.4553 | -0.0349 | -0.8002 | 0.4600 | 0.7653 | -49.0237 | -42.5545 | -2.9038 | -2.8422 |
| 0.3818 | 0.8 | 100 | 0.3747 | -0.1730 | -3.5887 | 0.4600 | 3.4157 | -76.9091 | -43.9359 | -2.8759 | -2.8145 |
| 0.3123 | 1.2 | 150 | 0.3744 | -0.2403 | -4.3676 | 0.4600 | 4.1273 | -84.6980 | -44.6088 | -2.8742 | -2.8132 |
| 0.364 | 1.6 | 200 | 0.3744 | -0.2016 | -4.5800 | 0.4600 | 4.3784 | -86.8216 | -44.2215 | -2.8745 | -2.8130 |
| 0.4332 | 2.0 | 250 | 0.3743 | -0.2684 | -4.8731 | 0.4600 | 4.6046 | -89.7525 | -44.8898 | -2.8737 | -2.8118 |
| 0.3986 | 2.4 | 300 | 0.3743 | -0.1931 | -5.0362 | 0.4600 | 4.8430 | -91.3835 | -44.1367 | -2.8747 | -2.8125 |
| 0.3986 | 2.8 | 350 | 0.3743 | -0.1846 | -5.1505 | 0.4600 | 4.9659 | -92.5268 | -44.0517 | -2.8745 | -2.8120 |
| 0.4506 | 3.2 | 400 | 0.3743 | -0.1881 | -5.2928 | 0.4600 | 5.1047 | -93.9497 | -44.0868 | -2.8736 | -2.8107 |
| 0.4505 | 3.6 | 450 | 0.3743 | -0.2250 | -5.5587 | 0.4600 | 5.3337 | -96.6092 | -44.4557 | -2.8724 | -2.8094 |
| 0.4332 | 4.0 | 500 | 0.3743 | -0.4284 | -5.9879 | 0.4600 | 5.5595 | -100.9007 | -46.4892 | -2.8698 | -2.8066 |
| 0.3292 | 4.4 | 550 | 0.3743 | -0.3669 | -5.9892 | 0.4600 | 5.6223 | -100.9135 | -45.8741 | -2.8695 | -2.8063 |
| 0.3639 | 4.8 | 600 | 0.3743 | -0.2855 | -5.9594 | 0.4600 | 5.6739 | -100.6163 | -45.0607 | -2.8699 | -2.8066 |
| 0.4505 | 5.2 | 650 | 0.3743 | -0.3591 | -6.0896 | 0.4600 | 5.7305 | -101.9183 | -45.7970 | -2.8685 | -2.8052 |
| 0.4505 | 5.6 | 700 | 0.3743 | -0.3292 | -6.0868 | 0.4600 | 5.7576 | -101.8900 | -45.4977 | -2.8687 | -2.8054 |
| 0.3639 | 6.0 | 750 | 0.3743 | -0.3284 | -6.1008 | 0.4600 | 5.7724 | -102.0299 | -45.4898 | -2.8683 | -2.8049 |
| 0.2426 | 6.4 | 800 | 0.3743 | -0.3283 | -6.0983 | 0.4600 | 5.7700 | -102.0044 | -45.4881 | -2.8684 | -2.8051 |
| 0.5025 | 6.8 | 850 | 0.3743 | -0.3251 | -6.0987 | 0.4600 | 5.7737 | -102.0092 | -45.4562 | -2.8685 | -2.8051 |
| 0.3119 | 7.2 | 900 | 0.3743 | -0.3297 | -6.1009 | 0.4600 | 5.7712 | -102.0308 | -45.5028 | -2.8684 | -2.8050 |
| 0.3466 | 7.6 | 950 | 0.3743 | -0.3291 | -6.1017 | 0.4600 | 5.7727 | -102.0393 | -45.4965 | -2.8684 | -2.8050 |
| 0.3812 | 8.0 | 1000 | 0.3743 | -0.3291 | -6.1017 | 0.4600 | 5.7727 | -102.0393 | -45.4965 | -2.8684 | -2.8050 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.0.0+cu117
- Datasets 3.0.0
- Tokenizers 0.19.1