gemma7b-summarize-gemini1_5flash-256k
This model is a fine-tuned version of google/gemma-7b on the llama-duo/synth_summarize_dataset_dedup dataset. It achieves the following results on the evaluation set:
- Loss: 2.4690
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9973 | 0.9988 | 414 | 2.5221 |
| 0.9036 | 2.0 | 829 | 2.4358 |
| 0.7651 | 2.9988 | 1243 | 2.3987 |
| 0.7192 | 4.0 | 1658 | 2.3970 |
| 0.6986 | 4.9988 | 2072 | 2.4163 |
| 0.6737 | 6.0 | 2487 | 2.4236 |
| 0.6633 | 6.9988 | 2901 | 2.4494 |
| 0.661 | 8.0 | 3316 | 2.4621 |
| 0.643 | 8.9988 | 3730 | 2.4791 |
| 0.6511 | 9.9879 | 4140 | 2.4690 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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Model tree for llama-duo/gemma7b-summarize-gemini1_5flash-256k
Base model
google/gemma-7b