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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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- alignment-handbook |
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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-7b |
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datasets: |
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- chansung/no_robots_only_coding |
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model-index: |
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- name: gemma-7b-sft-qlora-no-robots15 |
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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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# gemma-7b-sft-qlora-no-robots15 |
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This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the chansung/no_robots_only_coding dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2830 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 8 |
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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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| 21.906 | 0.91 | 5 | 7.6533 | |
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| 13.5603 | 2.0 | 11 | 6.6442 | |
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| 10.2605 | 2.91 | 16 | 6.0815 | |
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| 9.9129 | 4.0 | 22 | 3.1148 | |
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| 4.5895 | 4.91 | 27 | 1.6583 | |
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| 1.6316 | 6.0 | 33 | 1.4155 | |
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| 1.4115 | 6.91 | 38 | 1.3543 | |
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| 1.2971 | 8.0 | 44 | 1.3133 | |
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| 1.1321 | 8.91 | 49 | 1.2903 | |
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| 0.9739 | 10.0 | 55 | 1.2820 | |
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| 0.917 | 10.91 | 60 | 1.2888 | |
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| 0.8541 | 12.0 | 66 | 1.2781 | |
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| 0.8659 | 12.91 | 71 | 1.2892 | |
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| 0.8354 | 13.64 | 75 | 1.2830 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
