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---
language:
- en
library_name: peft
tags:
- generated_from_trainer
datasets:
- GEM/viggo
base_model: microsoft/phi-2
model-index:
- name: phi-2
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. -->
# phi-2
This model is a fine-tuned version of [microsoftl](https://huggingface.co/microsoftl) on the GEM/viggo dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2330
## 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: 2.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.917 | 0.04 | 50 | 1.4649 |
| 0.7037 | 0.08 | 100 | 0.4905 |
| 0.4209 | 0.12 | 150 | 0.3564 |
| 0.3534 | 0.16 | 200 | 0.3127 |
| 0.311 | 0.2 | 250 | 0.2940 |
| 0.2944 | 0.24 | 300 | 0.2798 |
| 0.2838 | 0.27 | 350 | 0.2710 |
| 0.2744 | 0.31 | 400 | 0.2634 |
| 0.2657 | 0.35 | 450 | 0.2577 |
| 0.2692 | 0.39 | 500 | 0.2513 |
| 0.263 | 0.43 | 550 | 0.2475 |
| 0.2664 | 0.47 | 600 | 0.2451 |
| 0.2535 | 0.51 | 650 | 0.2421 |
| 0.2594 | 0.55 | 700 | 0.2396 |
| 0.234 | 0.59 | 750 | 0.2379 |
| 0.2383 | 0.63 | 800 | 0.2361 |
| 0.2419 | 0.67 | 850 | 0.2350 |
| 0.2448 | 0.71 | 900 | 0.2337 |
| 0.241 | 0.74 | 950 | 0.2332 |
| 0.219 | 0.78 | 1000 | 0.2330 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 |