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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ |
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model-index: |
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- name: shakespeare-ft |
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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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# Cite |
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This model is trained from the code in this [GitHub](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/qlora) |
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# shakespeare-ft |
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This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an [Lambent/shakespeare_sonnets_backtranslated](https://huggingface.co/datasets/Lambent/shakespeare_sonnets_backtranslated) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7122 |
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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_steps: 2 |
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- num_epochs: 16 |
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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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| 1.3058 | 0.97 | 15 | 1.2255 | |
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| 1.017 | 2.0 | 31 | 1.1220 | |
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| 0.9377 | 2.97 | 46 | 1.0527 | |
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| 0.7699 | 4.0 | 62 | 0.9921 | |
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| 0.728 | 4.97 | 77 | 0.9438 | |
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| 0.6098 | 6.0 | 93 | 0.8995 | |
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| 0.5781 | 6.97 | 108 | 0.8649 | |
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| 0.4823 | 8.0 | 124 | 0.8288 | |
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| 0.4598 | 8.97 | 139 | 0.8065 | |
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| 0.3866 | 10.0 | 155 | 0.7736 | |
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| 0.3693 | 10.97 | 170 | 0.7525 | |
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| 0.3165 | 12.0 | 186 | 0.7422 | |
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| 0.312 | 12.97 | 201 | 0.7276 | |
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| 0.2761 | 14.0 | 217 | 0.7160 | |
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| 0.2815 | 14.97 | 232 | 0.7121 | |
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| 0.2463 | 15.48 | 240 | 0.7122 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.3 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
