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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-2b |
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datasets: |
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- llama-duo/synth_summarize_dataset_dedup |
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model-index: |
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- name: gemma2b-summarize-gemini1_5flash-64k |
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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-summarize-gemini1_5flash-64k |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the llama-duo/synth_summarize_dataset_dedup dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7177 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 2 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 128 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 1.518 | 0.9905 | 52 | 2.7709 | |
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| 1.1423 | 2.0 | 105 | 2.6595 | |
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| 1.0681 | 2.9905 | 157 | 2.6406 | |
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| 1.0335 | 4.0 | 210 | 2.6427 | |
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| 1.0079 | 4.9905 | 262 | 2.6459 | |
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| 0.9837 | 6.0 | 315 | 2.6574 | |
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| 0.966 | 6.9905 | 367 | 2.6700 | |
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| 0.9474 | 8.0 | 420 | 2.6799 | |
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| 0.9406 | 8.9905 | 472 | 2.6883 | |
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| 0.9245 | 10.0 | 525 | 2.6975 | |
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| 0.9208 | 10.9905 | 577 | 2.7079 | |
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| 0.9195 | 12.0 | 630 | 2.7148 | |
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| 0.9212 | 12.9905 | 682 | 2.7154 | |
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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 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
