Model save
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README.md
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---
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license: llama2
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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: codellama/CodeLlama-13b-hf
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model-index:
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- name: codellama13B-noautogen-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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# codellama13B-noautogen-StaproCoder
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This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3758
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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: 6
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- eval_batch_size: 6
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 24
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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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| 0.417 | 0.05 | 100 | 0.5468 |
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| 0.5187 | 0.1 | 200 | 0.4993 |
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| 0.3279 | 0.15 | 300 | 0.4787 |
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| 0.452 | 0.2 | 400 | 0.4551 |
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| 0.4738 | 0.25 | 500 | 0.4402 |
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| 0.4338 | 0.3 | 600 | 0.4263 |
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| 0.4024 | 0.35 | 700 | 0.4183 |
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| 0.4268 | 0.4 | 800 | 0.4082 |
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| 0.3572 | 0.45 | 900 | 0.4014 |
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| 0.3584 | 0.5 | 1000 | 0.3967 |
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| 0.359 | 0.55 | 1100 | 0.3913 |
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| 0.3023 | 0.6 | 1200 | 0.3865 |
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| 0.2707 | 0.65 | 1300 | 0.3820 |
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| 0.2918 | 0.7 | 1400 | 0.3790 |
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| 0.3188 | 0.75 | 1500 | 0.3757 |
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| 0.167 | 0.8 | 1600 | 0.3741 |
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| 0.2962 | 0.85 | 1700 | 0.3742 |
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| 0.2603 | 0.9 | 1800 | 0.3747 |
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| 0.2544 | 0.95 | 1900 | 0.3755 |
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| 0.254 | 1.0 | 2000 | 0.3758 |
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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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