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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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- generated_from_trainer |
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base_model: google/gemma-2b |
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model-index: |
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- name: Gemma2B-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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# Gemma2B-StaproCoder |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2988 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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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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| 0.733 | 0.05 | 100 | 0.5294 | |
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| 0.5149 | 0.1 | 200 | 0.4275 | |
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| 0.2925 | 0.15 | 300 | 0.3854 | |
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| 0.3588 | 0.2 | 400 | 0.3794 | |
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| 0.3145 | 0.25 | 500 | 0.3766 | |
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| 0.4036 | 0.3 | 600 | 0.3728 | |
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| 0.4822 | 0.35 | 700 | 0.3553 | |
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| 0.3456 | 0.4 | 800 | 0.3428 | |
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| 0.3978 | 0.45 | 900 | 0.3367 | |
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| 0.2692 | 0.5 | 1000 | 0.3365 | |
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| 0.4038 | 0.55 | 1100 | 0.3203 | |
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| 0.3345 | 0.6 | 1200 | 0.3210 | |
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| 0.2668 | 0.65 | 1300 | 0.3130 | |
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| 0.2617 | 0.7 | 1400 | 0.3103 | |
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| 0.2657 | 0.75 | 1500 | 0.3099 | |
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| 0.2633 | 0.8 | 1600 | 0.3041 | |
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| 0.4033 | 0.85 | 1700 | 0.3045 | |
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| 0.2208 | 0.9 | 1800 | 0.3017 | |
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| 0.2646 | 0.95 | 1900 | 0.2989 | |
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| 0.3054 | 1.0 | 2000 | 0.2988 | |
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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 |
