---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
- dolly
- ipex
- max series gpu
base_model: microsoft/phi-1_5
datasets:
- generator
model-index:
- name: lora_phi-1_5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# lora_phi-1_5

This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3998

## Model description

This is a fine-tuned version of the [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) model using Parameter Efficient Fine Tuning (PEFT) with Low Rank Adaptation (LoRA) on Intel(R) Data Center GPU Max 1100 and Intel(R) Xeon(R) Platinum 8480+ CPU .
This model can be used for various text generation tasks including chatbots, content creation, and other NLP applications.


## Training Hardware

This model was trained using: GPU:
- Intel(R) Data Center GPU Max 1100
- CPU: Intel(R) Xeon(R) Platinum 8480+


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 593

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.7756        | 0.8065 | 100  | 2.5791          |
| 2.558         | 1.6129 | 200  | 2.4656          |
| 2.4521        | 2.4194 | 300  | 2.4294          |
| 2.4589        | 3.2258 | 400  | 2.4103          |
| 2.4248        | 4.0323 | 500  | 2.3998          |


## Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.1
- Tokenizers 0.19.1