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---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-128k-instruct
datasets:
- scitldr
model-index:
- name: Summarization-Phi-3
results: []
---
<!-- 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. -->
# Summarization-Phi-3
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1554
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.0689 | 0.2510 | 500 | 2.1439 |
| 2.0455 | 0.5020 | 1000 | 2.1388 |
| 2.0665 | 0.7530 | 1500 | 2.1349 |
| 2.0481 | 1.0040 | 2000 | 2.1308 |
| 1.9051 | 1.2550 | 2500 | 2.1573 |
| 1.8524 | 1.5060 | 3000 | 2.1588 |
| 1.8247 | 1.7570 | 3500 | 2.1554 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 |