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README.md
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---
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license: bigscience-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: WizardLM/WizardCoder-15B-V1.0
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model-index:
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- name: Wizardcoder13B-StaproCoder
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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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# Wizardcoder13B-StaproCoder
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This model is a fine-tuned version of [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2314
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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: 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: cosine
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- lr_scheduler_warmup_ratio: 0.1
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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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| 0.2997 | 0.05 | 100 | 0.3043 |
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| 0.3811 | 0.1 | 200 | 0.2913 |
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| 0.242 | 0.15 | 300 | 0.2843 |
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| 0.2858 | 0.2 | 400 | 0.2756 |
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| 0.1813 | 0.25 | 500 | 0.2758 |
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| 0.2717 | 0.3 | 600 | 0.2656 |
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| 0.2431 | 0.35 | 700 | 0.2660 |
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| 0.3395 | 0.4 | 800 | 0.4781 |
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| 0.3274 | 0.45 | 900 | 0.2493 |
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| 0.1886 | 0.5 | 1000 | 0.2451 |
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| 0.1963 | 0.55 | 1100 | 0.2422 |
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| 0.2548 | 0.6 | 1200 | 0.2401 |
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| 0.3505 | 0.65 | 1300 | 0.2363 |
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| 0.2788 | 0.7 | 1400 | 0.2344 |
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| 0.186 | 0.75 | 1500 | 0.2323 |
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| 0.1847 | 0.8 | 1600 | 0.2325 |
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| 0.2109 | 0.85 | 1700 | 0.2313 |
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| 0.2144 | 0.9 | 1800 | 0.2316 |
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| 0.2038 | 0.95 | 1900 | 0.2316 |
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| 0.2071 | 1.0 | 2000 | 0.2314 |
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### Framework versions
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- PEFT 0.9.0
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- Transformers 4.39.0.dev0
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- Pytorch 2.2.1+cu121
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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