Llama2_AAID_structured_train
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: 0.4973
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
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.1654 | 0.0109 | 10 | 0.5499 |
0.4462 | 0.0219 | 20 | 0.5070 |
0.3949 | 0.0328 | 30 | 0.4973 |
0.3903 | 0.0438 | 40 | 0.5445 |
0.3617 | 0.0547 | 50 | 0.5150 |
0.3346 | 0.0656 | 60 | 0.5359 |
0.3284 | 0.0766 | 70 | 0.5421 |
0.3215 | 0.0875 | 80 | 0.5326 |
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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Model tree for Holmeister/Llama2_AAID_structured_train
Base model
meta-llama/Llama-2-7b-hf