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--- |
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license: other |
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base_model: google/gemma-7b |
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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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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/OpenHermes-2.5-1k-longest |
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model-index: |
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- name: gemma-7b-sft-full-longest-1k-v0 |
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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-full-longest-1k-v0 |
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This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co./google/gemma-7b) on the HuggingFaceH4/OpenHermes-2.5-1k-longest dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7445 |
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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: 2e-06 |
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- train_batch_size: 4 |
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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: 8 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 64 |
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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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- 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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| 5.6993 | 1.0 | 6 | 2.8191 | |
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| 3.3379 | 2.0 | 12 | 2.2503 | |
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| 2.8978 | 3.0 | 18 | 2.0730 | |
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| 2.7495 | 4.0 | 24 | 1.9771 | |
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| 2.5265 | 5.0 | 30 | 1.9129 | |
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| 2.4727 | 6.0 | 36 | 1.8681 | |
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| 2.443 | 7.0 | 42 | 1.8344 | |
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| 2.3432 | 8.0 | 48 | 1.8083 | |
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| 2.3291 | 9.0 | 54 | 1.7878 | |
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| 2.2843 | 10.0 | 60 | 1.7719 | |
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| 2.2529 | 11.0 | 66 | 1.7595 | |
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| 2.2723 | 12.0 | 72 | 1.7509 | |
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| 2.2302 | 13.0 | 78 | 1.7465 | |
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| 2.2224 | 14.0 | 84 | 1.7448 | |
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| 2.2309 | 15.0 | 90 | 1.7445 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.1 |
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