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--- |
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license: mit |
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library_name: peft |
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tags: |
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- trl |
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- kto |
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- KTO |
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- WeniGPT |
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- generated_from_trainer |
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base_model: HuggingFaceH4/zephyr-7b-beta |
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model-index: |
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- name: WeniGPT-Agents-Zephyr-1.0.1-KTO |
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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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# WeniGPT-Agents-Zephyr-1.0.1-KTO |
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This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4733 |
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- Rewards/chosen: -149.7694 |
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- Logps/chosen: -1804.1348 |
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- Rewards/rejected: -137.1733 |
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- Logps/rejected: -1664.4484 |
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- Kl: 0.0 |
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- Rewards/margins: -9.4252 |
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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: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.03 |
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- training_steps: 145 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Kl | Rewards/margins | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:--------------:|:------:|:---------------:| |
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| 0.4081 | 0.34 | 50 | 0.3897 | -0.9805 | -316.2467 | -3.9926 | -332.6412 | 1.7103 | 3.1516 | |
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| 0.4304 | 0.68 | 100 | 0.4733 | -149.7694 | -1804.1348 | -137.1733 | -1664.4484 | 0.0 | -9.4252 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
