---
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-APPS
  results: []
datasets:
- codeparrot/apps
pipeline_tag: text-generation
---

<!-- 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. -->

# CodePhi-3-mini-4k-instruct-APPS

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: 0.7651

## 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: 1200

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8306        | 0.0833 | 100  | 0.8445          |
| 0.6325        | 0.1667 | 200  | 0.8164          |
| 0.6696        | 0.25   | 300  | 0.7934          |
| 0.643         | 0.3333 | 400  | 0.7815          |
| 0.778         | 0.4167 | 500  | 0.7737          |
| 0.7273        | 0.5    | 600  | 0.7696          |
| 0.712         | 0.5833 | 700  | 0.7676          |
| 0.6477        | 0.6667 | 800  | 0.7658          |
| 0.6245        | 0.75   | 900  | 0.7652          |
| 0.6959        | 0.8333 | 1000 | 0.7650          |
| 0.7236        | 0.9167 | 1100 | 0.7650          |
| 0.6774        | 1.0    | 1200 | 0.7651          |


### Framework versions

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