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--- |
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license: bigcode-openrail-m |
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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: bigcode/starcoderbase-1b |
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model-index: |
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- name: peft-starcoder-lora-a100 |
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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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# peft-starcoder-lora-a100 |
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This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0584 |
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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: 0.0005 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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_steps: 30 |
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- training_steps: 2000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.9661 | 0.05 | 100 | 0.9191 | |
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| 0.99 | 0.1 | 200 | 0.9444 | |
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| 0.6519 | 0.15 | 300 | 0.9675 | |
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| 0.8967 | 0.2 | 400 | 0.9820 | |
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| 0.9498 | 0.25 | 500 | 0.9725 | |
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| 0.574 | 0.3 | 600 | 1.0097 | |
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| 0.7988 | 0.35 | 700 | 1.0181 | |
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| 0.8264 | 0.4 | 800 | 1.0122 | |
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| 0.5209 | 0.45 | 900 | 1.0173 | |
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| 0.7516 | 0.5 | 1000 | 1.0316 | |
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| 0.7716 | 0.55 | 1100 | 1.0355 | |
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| 0.4749 | 0.6 | 1200 | 1.0538 | |
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| 0.6964 | 0.65 | 1300 | 1.0393 | |
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| 0.6841 | 0.7 | 1400 | 1.0459 | |
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| 0.5397 | 0.75 | 1500 | 1.0513 | |
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| 0.6572 | 0.8 | 1600 | 1.0543 | |
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| 0.6004 | 0.85 | 1700 | 1.0606 | |
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| 0.6017 | 0.9 | 1800 | 1.0621 | |
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| 0.5555 | 0.95 | 1900 | 1.0611 | |
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| 0.5882 | 1.0 | 2000 | 1.0584 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
