---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/Phi-3-mini-128k-instruct
model-index:
- name: CodePhi-3-mini-128k-instruct-appsloraN1.5k
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# CodePhi-3-mini-128k-instruct-appsloraN1.5k

This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6506

## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5976        | 0.0667 | 100  | 0.7027          |
| 0.6556        | 0.1333 | 200  | 0.6810          |
| 0.6492        | 0.2    | 300  | 0.6706          |
| 0.5924        | 0.2667 | 400  | 0.6644          |
| 0.618         | 0.3333 | 500  | 0.6599          |
| 0.6025        | 0.4    | 600  | 0.6566          |
| 0.6049        | 0.4667 | 700  | 0.6539          |
| 0.5651        | 0.5333 | 800  | 0.6526          |
| 0.5803        | 0.6    | 900  | 0.6516          |
| 0.5371        | 0.6667 | 1000 | 0.6509          |
| 0.6274        | 0.7333 | 1100 | 0.6507          |
| 0.6122        | 0.8    | 1200 | 0.6506          |
| 0.5815        | 0.8667 | 1300 | 0.6506          |
| 0.6261        | 0.9333 | 1400 | 0.6505          |
| 0.5859        | 1.0    | 1500 | 0.6506          |


### Framework versions

- PEFT 0.11.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1