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--- |
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license: mit |
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base_model: BramVanroy/fietje-2b-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: fietje-2b-dpo-lr2.0e-6-beta0.2-gradaccum2-v6 |
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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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# fietje-2b-dpo-lr2.0e-6-beta0.2-gradaccum2-v6 |
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This model is a fine-tuned version of [BramVanroy/fietje-2b-sft](https://huggingface.co/BramVanroy/fietje-2b-sft) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2842 |
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- Rewards/chosen: -1.1549 |
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- Rewards/rejected: -3.6363 |
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- Rewards/accuracies: 0.8867 |
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- Rewards/margins: 2.4815 |
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- Logps/rejected: -657.6813 |
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- Logps/chosen: -451.3364 |
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- Logits/rejected: -1.2868 |
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- Logits/chosen: -1.3528 |
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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: 2e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1.0 |
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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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| 0.2515 | 1.0 | 1166 | 0.2842 | -1.1549 | -3.6363 | 0.8867 | 2.4815 | -657.6813 | -451.3364 | -1.2868 | -1.3528 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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