peft safetensors llama-factory lora generated_from_trainer base_model:microsoft/phi-2 license:other region:us
---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: delphi
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# delphi

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the alpaca_gpt4_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8625

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0325        | 0.07  | 200  | 0.8922          |
| 0.9136        | 0.14  | 400  | 0.8801          |
| 0.9043        | 0.21  | 600  | 0.8753          |
| 0.8903        | 0.27  | 800  | 0.8711          |
| 0.9056        | 0.34  | 1000 | 0.8683          |
| 0.9001        | 0.41  | 1200 | 0.8673          |
| 0.8949        | 0.48  | 1400 | 0.8668          |
| 0.8917        | 0.55  | 1600 | 0.8656          |
| 0.8951        | 0.62  | 1800 | 0.8648          |
| 0.9014        | 0.68  | 2000 | 0.8637          |
| 0.8874        | 0.75  | 2200 | 0.8630          |
| 0.8968        | 0.82  | 2400 | 0.8628          |
| 0.8755        | 0.89  | 2600 | 0.8626          |
| 0.9029        | 0.96  | 2800 | 0.8625          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0