Llama2_AAID_structured_train_final
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the AAID_structured dataset. It achieves the following results on the evaluation set:
- Loss: 0.5186
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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.462 | 0.0438 | 40 | 0.5250 |
0.3971 | 0.0875 | 80 | 0.5608 |
0.3292 | 0.1313 | 120 | 0.5477 |
0.3107 | 0.1750 | 160 | 0.5437 |
0.3033 | 0.2188 | 200 | 0.5186 |
0.2864 | 0.2625 | 240 | 0.5211 |
0.2807 | 0.3063 | 280 | 0.5293 |
0.2748 | 0.3500 | 320 | 0.5545 |
0.2685 | 0.3938 | 360 | 0.5562 |
0.2511 | 0.4375 | 400 | 0.5585 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
meta-llama/Llama-2-7b-hf