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--- |
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license: apache-2.0 |
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base_model: AmberYifan/mistral-safe-sft-full |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mistral-sft-kcenter-5k |
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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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# mistral-sft-kcenter-5k |
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This model is a fine-tuned version of [AmberYifan/mistral-safe-sft-full](https://huggingface.co/AmberYifan/mistral-safe-sft-full) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1925 |
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- Rewards/real: 5.5330 |
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- Rewards/generated: -4.6681 |
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- Rewards/accuracies: 0.9922 |
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- Rewards/margins: 10.2011 |
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- Logps/generated: -304.9019 |
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- Logps/real: -157.3288 |
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- Logits/generated: -2.8831 |
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- Logits/real: -2.8163 |
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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: 5e-07 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 32 |
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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: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real | |
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|:-------------:|:------:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:| |
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| 0.1804 | 0.6369 | 100 | 0.1925 | 5.5330 | -4.6681 | 0.9922 | 10.2011 | -304.9019 | -157.3288 | -2.8831 | -2.8163 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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