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---
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")> |