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--- |
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license: llama3 |
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base_model: tsavage68/UTI_L3_1000steps_1e5rate_SFT |
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tags: |
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- trl |
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- dpo |
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- generated_from_trainer |
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model-index: |
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- name: UTI2_L3_1000steps_1e5rate_03beta_CSFTDPO |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# UTI2_L3_1000steps_1e5rate_03beta_CSFTDPO |
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This model is a fine-tuned version of [tsavage68/UTI_L3_1000steps_1e5rate_SFT](https://huggingface.co/tsavage68/UTI_L3_1000steps_1e5rate_SFT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6931 |
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- Rewards/chosen: 0.0 |
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- Rewards/rejected: 0.0 |
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- Rewards/accuracies: 0.0 |
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- Rewards/margins: 0.0 |
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- Logps/rejected: 0.0 |
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- Logps/chosen: 0.0 |
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- Logits/rejected: -1.1794 |
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- Logits/chosen: -1.1794 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.6931 | 0.3333 | 25 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 0.6667 | 50 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 1.0 | 75 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 1.3333 | 100 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 1.6667 | 125 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 2.0 | 150 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 2.3333 | 175 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 2.6667 | 200 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 3.0 | 225 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 3.3333 | 250 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 3.6667 | 275 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 4.0 | 300 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 4.3333 | 325 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 4.6667 | 350 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 5.0 | 375 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 5.3333 | 400 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 5.6667 | 425 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 6.0 | 450 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 6.3333 | 475 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 6.6667 | 500 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 7.0 | 525 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 7.3333 | 550 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 7.6667 | 575 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 8.0 | 600 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 8.3333 | 625 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 8.6667 | 650 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 9.0 | 675 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 9.3333 | 700 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 9.6667 | 725 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 10.0 | 750 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 10.3333 | 775 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 10.6667 | 800 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 11.0 | 825 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 11.3333 | 850 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 11.6667 | 875 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 12.0 | 900 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 12.3333 | 925 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 12.6667 | 950 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 13.0 | 975 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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| 0.6931 | 13.3333 | 1000 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.1794 | -1.1794 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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