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--- |
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base_model: bofenghuang/vigogne-2-13b-instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: PointCon-Vigogne-13B-LoRA |
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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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# PointCon-Vigogne-13B-LoRA |
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This model is a fine-tuned version of [bofenghuang/vigogne-2-13b-instruct](https://huggingface.co/bofenghuang/vigogne-2-13b-instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8656 |
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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-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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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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- num_epochs: 1 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.0885 | 0.1 | 30 | 2.0357 | |
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| 2.0024 | 0.19 | 60 | 1.9733 | |
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| 1.9995 | 0.29 | 90 | 1.9406 | |
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| 1.9752 | 0.38 | 120 | 1.9285 | |
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| 1.9235 | 0.48 | 150 | 1.9060 | |
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| 1.9345 | 0.57 | 180 | 1.8924 | |
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| 1.8576 | 0.67 | 210 | 1.8818 | |
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| 1.8693 | 0.76 | 240 | 1.8734 | |
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| 1.8686 | 0.86 | 270 | 1.8695 | |
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| 1.8814 | 0.95 | 300 | 1.8656 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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