---
license: apache-2.0
datasets:
- iamtarun/python_code_instructions_18k_alpaca
language:
- en
library_name: peft
pipeline_tag: text2text-generation
tags:
- code
---


Here's a brief description of my project.

## Table of Contents

- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
  - [Installation](#installation)
  - [Usage](#usage)
- [Documentation](#documentation)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgements](#acknowledgements)

## Introduction

colab_code_generator_FT_code_gen_UT, an instruction-following large language model trained on the Google Colab Pro with T4 GPU and fine-tuned on 'Salesforce/codegen-350M-mono' that is licensed for commercial use. Code Generator_UT is trained on ~19k instructions/response fine-tuning records from 'iamtarun/python_code_instructions_18k_alpaca'.

### Loading the fine-tuned Code Generator
```
from peft import AutoPeftModelForCausalLM>
test_model_UT = AutoPeftModelForCausalLM.from_pretrained("01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT")
test_tokenizer_UT = AutoTokenizer.from_pretrained("01GangaPutraBheeshma/colab_code_generator_FT_code_gen_UT")```