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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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- zhihu |
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base_model: 01-ai/Yi-6B |
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model-index: |
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- name: Yi-6B-zhihu7 |
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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-zhihu7 |
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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 zhihu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5970 |
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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-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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- 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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| 2.6831 | 1.0 | 96 | 2.6346 | |
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| 2.6684 | 2.0 | 192 | 2.6288 | |
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| 2.687 | 3.0 | 288 | 2.6192 | |
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| 2.6597 | 4.0 | 384 | 2.6109 | |
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| 2.6019 | 5.0 | 480 | 2.6054 | |
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| 2.6118 | 6.0 | 576 | 2.6022 | |
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| 2.7286 | 7.0 | 672 | 2.6001 | |
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| 2.6341 | 8.0 | 768 | 2.5987 | |
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| 2.572 | 9.0 | 864 | 2.5979 | |
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| 2.622 | 10.0 | 960 | 2.5974 | |
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| 2.6404 | 11.0 | 1056 | 2.5972 | |
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| 2.6607 | 12.0 | 1152 | 2.5971 | |
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| 2.5324 | 13.0 | 1248 | 2.5971 | |
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| 2.5472 | 14.0 | 1344 | 2.5970 | |
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| 2.539 | 15.0 | 1440 | 2.5970 | |
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| 2.5757 | 16.0 | 1536 | 2.5971 | |
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| 2.6495 | 17.0 | 1632 | 2.5970 | |
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| 2.5647 | 18.0 | 1728 | 2.5970 | |
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| 2.5605 | 19.0 | 1824 | 2.5970 | |
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| 2.6608 | 20.0 | 1920 | 2.5970 | |
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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 |
