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---
license: mit
library_name: peft
tags:
- generated_from_trainer
datasets:
- scitldr
base_model: microsoft/phi-1_5
model-index:
- name: Phi-1.5-Summarization-QLoRA
results: []
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Phi-1.5 Summarization (QLoRA)
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5866
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5683 | 0.25 | 500 | 2.6196 |
| 2.5308 | 0.5 | 1000 | 2.5992 |
| 2.558 | 0.75 | 1500 | 2.5886 |
| 2.4925 | 1.0 | 2000 | 2.5827 |
| 2.3252 | 1.26 | 2500 | 2.5948 |
| 2.3128 | 1.51 | 3000 | 2.5879 |
| 2.4622 | 1.76 | 3500 | 2.5866 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |