metadata
library_name: transformers
license: llama3
base_model: tsavage68/IE_L3_1000steps_1e6rate_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: IE_L3_1000steps_1e8rate_03beta_cSFTDPO
results: []
IE_L3_1000steps_1e8rate_03beta_cSFTDPO
This model is a fine-tuned version of tsavage68/IE_L3_1000steps_1e6rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6864
- Rewards/chosen: -0.0017
- Rewards/rejected: -0.0201
- Rewards/accuracies: 0.4050
- Rewards/margins: 0.0184
- Logps/rejected: -75.6942
- Logps/chosen: -82.8034
- Logits/rejected: -0.7975
- Logits/chosen: -0.7402
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6912 | 0.4 | 50 | 0.6940 | -0.0075 | -0.0104 | 0.4000 | 0.0029 | -75.6618 | -82.8226 | -0.7964 | -0.7393 |
| 0.6947 | 0.8 | 100 | 0.6925 | 0.0014 | -0.0057 | 0.3850 | 0.0070 | -75.6461 | -82.7931 | -0.7963 | -0.7394 |
| 0.6873 | 1.2 | 150 | 0.6982 | -0.0140 | -0.0096 | 0.3950 | -0.0044 | -75.6592 | -82.8444 | -0.7963 | -0.7393 |
| 0.6777 | 1.6 | 200 | 0.6892 | -0.0038 | -0.0171 | 0.4100 | 0.0134 | -75.6844 | -82.8103 | -0.7963 | -0.7393 |
| 0.6879 | 2.0 | 250 | 0.6890 | -0.0049 | -0.0185 | 0.3800 | 0.0136 | -75.6890 | -82.8142 | -0.7980 | -0.7411 |
| 0.6991 | 2.4 | 300 | 0.6849 | -0.0170 | -0.0393 | 0.4300 | 0.0223 | -75.7583 | -82.8544 | -0.7974 | -0.7404 |
| 0.678 | 2.8 | 350 | 0.6716 | -0.0122 | -0.0614 | 0.4900 | 0.0492 | -75.8319 | -82.8383 | -0.7967 | -0.7398 |
| 0.7072 | 3.2 | 400 | 0.6885 | -0.0120 | -0.0278 | 0.4350 | 0.0158 | -75.7200 | -82.8378 | -0.7974 | -0.7404 |
| 0.6858 | 3.6 | 450 | 0.6943 | -0.0160 | -0.0191 | 0.3450 | 0.0031 | -75.6910 | -82.8512 | -0.7974 | -0.7404 |
| 0.6815 | 4.0 | 500 | 0.6821 | -0.0089 | -0.0364 | 0.4300 | 0.0275 | -75.7484 | -82.8273 | -0.7972 | -0.7401 |
| 0.6857 | 4.4 | 550 | 0.6879 | -0.0086 | -0.0255 | 0.4000 | 0.0169 | -75.7121 | -82.8263 | -0.7972 | -0.7403 |
| 0.6825 | 4.8 | 600 | 0.6854 | -0.0203 | -0.0417 | 0.4150 | 0.0214 | -75.7663 | -82.8655 | -0.7968 | -0.7398 |
| 0.698 | 5.2 | 650 | 0.6921 | -0.0186 | -0.0277 | 0.4200 | 0.0091 | -75.7196 | -82.8597 | -0.7973 | -0.7401 |
| 0.6795 | 5.6 | 700 | 0.6885 | -0.0063 | -0.0217 | 0.3700 | 0.0154 | -75.6996 | -82.8189 | -0.7973 | -0.7402 |
| 0.6931 | 6.0 | 750 | 0.6875 | -0.0110 | -0.0282 | 0.4150 | 0.0172 | -75.7213 | -82.8344 | -0.7974 | -0.7404 |
| 0.6804 | 6.4 | 800 | 0.6888 | -0.0053 | -0.0191 | 0.3800 | 0.0137 | -75.6909 | -82.8156 | -0.7975 | -0.7402 |
| 0.6958 | 6.8 | 850 | 0.6864 | -0.0017 | -0.0201 | 0.4050 | 0.0184 | -75.6942 | -82.8034 | -0.7975 | -0.7402 |
| 0.6932 | 7.2 | 900 | 0.6864 | -0.0017 | -0.0201 | 0.4050 | 0.0184 | -75.6942 | -82.8034 | -0.7975 | -0.7402 |
| 0.6785 | 7.6 | 950 | 0.6864 | -0.0017 | -0.0201 | 0.4050 | 0.0184 | -75.6942 | -82.8034 | -0.7975 | -0.7402 |
| 0.6947 | 8.0 | 1000 | 0.6864 | -0.0017 | -0.0201 | 0.4050 | 0.0184 | -75.6942 | -82.8034 | -0.7975 | -0.7402 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.0.0+cu117
- Datasets 3.0.0
- Tokenizers 0.19.1