Llama-3.2-1B-Summarization-QLoRa
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the scitldr dataset. It achieves the following results on the evaluation set:
- Loss: 2.5899
Model description
Fine-tuned (QLoRa) Version of Meta-llama/Llama-3.2-1B for Summarization of scientific documents.
Intended uses & limitations
Summarization
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.4993 | 0.2008 | 200 | 2.5715 |
| 2.4748 | 0.4016 | 400 | 2.5674 |
| 2.4744 | 0.6024 | 600 | 2.5674 |
| 2.4646 | 0.8032 | 800 | 2.5558 |
| 2.4637 | 1.0040 | 1000 | 2.5539 |
| 2.1281 | 1.2048 | 1200 | 2.5904 |
| 2.1157 | 1.4056 | 1400 | 2.5928 |
| 2.0962 | 1.6064 | 1600 | 2.5855 |
| 2.0721 | 1.8072 | 1800 | 2.5899 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for pkbiswas/Llama-3.2-1B-Summarization-QLoRa
Base model
meta-llama/Llama-3.2-1B