---
datasets:
- mlabonne/Evol-Instruct-Python-26k
language:
- en
library_name: adapter-transformers
tags:
- code
---

## Model Details

### Model Description

- **Developed by:** Luthfantry Zamzam Muhammad
- **Model type:** large language model for code generation
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model:** llama2

### Model Sources 

- **Repository:** https://github.com/unslothai/unsloth
- **Developed by:** unsloth

### Model parameter

- r = 16, 
- target_modules = ["q_proj", "k_proj", "v_proj", "o_proj","gate_proj", "up_proj", "down_proj",],
- lora_alpha = 16,
- lora_dropout = 0, 
- bias = "none",    
- use_gradient_checkpointing = "unsloth", 
- random_state = 3407,
- use_rslora = False,  
- loftq_config = None, 

## Usage and limitations

This model is used to generate code based on commands given by the user. it should be noted that this model can generate many languages because it takes the initial model from llama2. However, after finetuning it is better at generating python code, because currently it is only trained with python code datasets. 

## How to Get Started with the Model

use link below to use model
https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-26k


#### Training Hyperparameters

- **Warmup_step:** 5
- **lr_scheduler_type:** linear
- **Learning Rate:** 0.0002
- **Batch Size:** 2
- **Weigh_decay:** 0.001
- **Epoch:** 60
- **Optimizer:** adamw_8bit

#### Testing Data

https://huggingface.co/datasets/google-research-datasets/mbpp/viewer/full

### Testing Document

https://docs.google.com/spreadsheets/d/1hr8R4nixQsDC5cGGENTOLUW1jPCS_lltVRIeOzenBvA/edit?usp=sharing

### Results

Berfore finetune 
- Accurary : 1,8%
- Consistensy : 0%

After fine tune 
- Accuracy : 49%
- Consistency : 100%