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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: google/gemma-2b |
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datasets: |
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- generator |
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model-index: |
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- name: gemma2b-classification-gpt4o-100k |
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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-classification-gpt4o-100k |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8001 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 3 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 48 |
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- total_eval_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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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 1.4266 | 0.9969 | 159 | 1.9880 | |
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| 1.3029 | 2.0 | 319 | 1.9710 | |
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| 1.2414 | 2.9969 | 478 | 1.9794 | |
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| 1.2012 | 4.0 | 638 | 2.0134 | |
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| 1.1513 | 4.9969 | 797 | 2.0583 | |
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| 1.0951 | 6.0 | 957 | 2.1084 | |
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| 1.0414 | 6.9969 | 1116 | 2.2094 | |
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| 1.0041 | 8.0 | 1276 | 2.3043 | |
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| 0.9481 | 8.9969 | 1435 | 2.3989 | |
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| 0.9006 | 10.0 | 1595 | 2.5173 | |
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| 0.8626 | 10.9969 | 1754 | 2.6419 | |
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| 0.8351 | 12.0 | 1914 | 2.7331 | |
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| 0.8265 | 12.9969 | 2073 | 2.7838 | |
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| 0.8167 | 14.0 | 2233 | 2.7990 | |
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| 0.8075 | 14.9530 | 2385 | 2.8001 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
