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--- |
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license: other |
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library_name: peft |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- ruozhiba |
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base_model: 01-ai/Yi-6B |
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model-index: |
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- name: Yi-6B-ruozhiba |
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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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# Yi-6B-ruozhiba |
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This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the ruozhiba dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.2495 |
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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: 4 |
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- eval_batch_size: 4 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.644 | 1.0 | 55 | 2.2811 | |
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| 1.9483 | 2.0 | 110 | 1.9384 | |
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| 1.7791 | 3.0 | 165 | 1.9109 | |
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| 1.6053 | 4.0 | 220 | 1.9560 | |
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| 1.3206 | 5.0 | 275 | 2.1674 | |
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| 0.9722 | 6.0 | 330 | 2.4236 | |
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| 0.7026 | 7.0 | 385 | 2.6935 | |
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| 0.4776 | 8.0 | 440 | 3.0005 | |
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| 0.3163 | 9.0 | 495 | 3.2703 | |
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| 0.2355 | 10.0 | 550 | 3.4524 | |
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| 0.1489 | 11.0 | 605 | 3.6805 | |
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| 0.1179 | 12.0 | 660 | 3.7960 | |
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| 0.0712 | 13.0 | 715 | 3.9715 | |
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| 0.0782 | 14.0 | 770 | 4.0284 | |
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| 0.0691 | 15.0 | 825 | 4.1576 | |
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| 0.0486 | 16.0 | 880 | 4.1690 | |
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| 0.0639 | 17.0 | 935 | 4.2094 | |
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| 0.0589 | 18.0 | 990 | 4.2436 | |
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| 0.042 | 19.0 | 1045 | 4.2491 | |
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| 0.0405 | 20.0 | 1100 | 4.2495 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.2.2+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
